Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan)

(1)

DEVELOPMENT OF LAND SUITABILITY EVALUATION

SYSTEM FOR COASTAL AQUACULTURE

USING ARTIFICIAL NEURAL NETWORK AND

GEOGRAPHICAL INFORMATION SYSTEMS

Case Study: Mahakam Delta, East Kalimantan

I KETUT SUTARGA

GRADUATE SCHOOL

(2)

ABSTRACT

I Ketut Sutarga (2005). Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan). Under the Supervision of I Wayan Astika and Antonius Bambang Wijanarto.

Land suitability is the aptitude of given type of land to support a certain use. Land suitability is one of key factors for the sustainability of aquaculture system. It can be determined by using matching methods between land suitability criteria and land characteristics, such as topography, hydrography, climate, water characteristic, risk, land cover, and spatial plan aspects. Evaluation of land for aquaculture suitability can be reached using a parametric approach, which is implemented by using the distinguish land characteristics and combination of it. Parametric approach uses the numeric value, which classifies the land based on individual characteristics.

The objectives of the research are to develop a land evaluation system on aquaculture suitability by using artificial neural network (ANN) and geographical information system (GIS) and to evaluate the performance of the new developed system as compared to GIS vector spatial analysis method. Four types of suitability were defined, i.e. highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N). The new developed GIS model uses the raster format and ANN classifier. The performance of GIS model was then evaluated, and compared to the vector map overlay that used GIS spatial analysis features, including suitability distribution, covered area, processing speed and validity.

The multilayer feedforward of the ANN with backpropagation learning algorithm was chosen to do the classification. GIS overlaid layer in form of spatial database was then converted into training dataset, validation dataset and prediction dataset. Training and validation dataset contains a pair of input and output, while the prediction dataset contains input data only.

To compare the two methods, the Mahakam Delta-East Kalimantan were selected as a case study. Ten thematic map layers of land characteristic were involved in both methods, i.e.: water salinity, dissolved oxygen, water pH, soil texture, soil drainage, distance to rivers, pollution risk, land cover type, land use plan and annual rainfall intensity. Each feature of thematic map layers were classified and scored using 1 to 4 ranges from worst to best, respectively.

The performance of ANN together with GIS was reliable, it includes the accuracy on training dataset of 97%, and validation of 96%. Distributions of each suitability of both methods are located on the same location in the study area. Area covered in the study area was 188,666.19 ha, consisting of 21,202.20 ha (11.24%) of

(3)

S1, 87,190.83 ha (46.21%) of S2, 6,958.26 ha (3.69%) of S3 and 61,251.12 ha of N. Only 0.19% of total area represents the “Unclassified Area” and 6.20% is “No Data”. Unclassified areas may be caused by less representative training dataset. “No Data” class represents the area that was not included in the data processing by raster data model. The research also proved that ANN together with GIS raster do the classification faster than vector spatial analysis.

(4)

DEVELOPMENT OF LAND SUITABILITY EVALUATION

SYSTEM FOR COASTAL AQUACULTURE USING

ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL

INFORMATION SYSTEMS

Case Study: Mahakam Delta - East Kalimantan

I KETUT SUTARGA

A Thesis submitted for the degree of Master of Science

of Bogor Agricultural University

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOURCES MANAGEMENT

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

(5)

Research Title : Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan) Student Name : I Ketut Sutarga

Student ID : G.051030131

Study Program : Master of Science in Information Technology for Natural Resources Management

Approved by, Advisory Board:

Dr. Ir. I Wayan Astika, M.Si Supervisor

Dr. Antonius Bambang Wijanarto Co-supervisor

Endorsed by,

(6)

STATEMENT

I, I Ketut Sutarga, hereby stated that this thesis entitled:

Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems

(Case Study: Mahakam Delta, East Kalimantan)

is the result of my own work during the period of April until September 2005 and it has not been published before. The content of the thesis has been examined by the advising committee and the external examiner.

Bogor, November 2005

(7)

ACKNOWLEDGEMENT

I would to extend my special thanks to the following people for their contribution to this thesis.

ϖ The research was completed under the supervision of Dr. Ir. I Wayan Astika, M.Si as supervisor and Dr. Antonius B. Wijanarto as co-supervisor who have kept me on track with their guidance, technical comments and constructive criticism through all months of my research.

ϖ To my external examiner Dr. Ir. Djoko Purwanto, DEA, for his corrections, suggestions and positive ideas of this thesis.

ϖ To the MIT-SEAMEO BIOTROP management and staffs, as well as IPB Post Graduate who have supported our administration, technical aspects and the facilities.

ϖ To the lecturers of MIT who taught me with important knowledge for my future prospects.

ϖ To Bakosurtanal, especially for chairman and the head of the Center for Marine Natural Resource Survey for giving me the opportunity and funding to study for higher education.

ϖ To my friends of MIT 2003 batch, I really appreciate our togetherness, solidarities, and support to finish my study. Finally I am really thank to my beloved wife Suratmi and my daughter Saras and my son Sena for your eternal love, support and patience. I dedicated this thesis to my office Bakosurtanal.

(8)

Engineer Diploma in Geodetic Engineering from the Faculty of Engineering, Gadjah Mada University, Yogyakarta in 1991. From 1989 to 1993, he worked for private engineering company in the field of surveying, mapping and consulting engineering. Since 1994 to present, he has been working for National Coordinating Agency for Surveys and Mapping (Bakosurtanal), Cibinong, West Java.

In 2003, I Ketut Sutarga received a financial support from the Center for Marine Natural Resource Surveys – Bakosurtanal to pursue his graduate study. He received his Master of Science in Information Technology for Natural Resources Management from Bogor Agricultural University – IPB in 2005. His thesis entitled “Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems (Case Study: Mahakam Delta, East Kalimantan)”.

(9)

STATEMENT ... I

ACKNOWLEDGEMENT... ii

CURRICULUM VITAE ... iii

ABSTRACT ...iv

TABLE OF CONTENTS ...v

LIST OF TABLES ...vi

LIST OF FIGURES ...vii

LIST OF APPENDICES ... viii

I. INTRODUCTION ...1

1.1. Background ...1

1.2. Objectives ...4

1.3. Problem Statement ...5

1.4. Research Output ...5

II. LITERATURE REVIEW ...7

2.1. Geographical Information Systems ...7

2.1.1. Spatial and Attribute Data ...7

2.1.2. Spatial Analysis ...9

2.1.2.1. Vector Data Analysis ...10

2.1.2.2. Raster Data Analysis ...11

2.2. Artificial Neural Network ...12

2.2.1. Artificial Neural Network Basics ...12

2.2.2. Multi Layer Feedforward-Backpropagation ...15

2.2.2.1. Backpropagation Model ...15

2.2.2.2. Backpropagation Learning Algorithm ...16

2.3. Land Suitability ...19

2.4. Coastal-Aquaculture ...21

………... 21

2.1.1. Aquaculture System ………21

2.1.2. Cultivable Species ……….…… 22

2.5. Previous Related Research ……… 23

III. RESEARCH METHODOLOGY ………25

(10)

3.3.1. Data Acquisition ……… 26

3.3.2. Field Data Processing ……… 28

3.3.3. Equipments ……… 29

3.4. Procedures ………. 30

3.4.1. Spatial Database Preparation ……… 30

3.4.2. Mapping Suitability by Vector Analysis ……….. 32

3.4.3. Map Conversion and Combination ……….. 34

3.4.4. Building Artificial Neural Network ………. 35

3.4.4.1. Training Process ……….. 35

3.4.4.2. Validation ……… 37

3.4.4.3. Map the Raster GIS and ANN Suitability …….. 38

3.5. Land Suitability Comparison ……… 40

IV. RESULT AND DISCUSSION ……….. 41

4.1. Performances of ANN and GIS Method ……… 41

4.1.1. Training Data Sets ………. 41

4.1.2. Validation Data Sets ……….. 42

4.2. Land Suitability Map of Raster GIS and ANN ………. 43

4.3. Land Suitability Analysis using Vector-Map Overlay ……….. 45

4.4. Comparison of Field Data and Land Suitability ……… 48

4.5. Comparison of The Two Methods ……… 50

4.5.1. Suitability Distributions ………. 50

4.5.2. Area Covers ……… ……….. 52

V. CONCLUSIONS ……… 56

5.1. Conclusions ……… 56

5.2. Recommendations ………. 57

REFERENCES ……….. 58

(11)

DEVELOPMENT OF LAND SUITABILITY EVALUATION

SYSTEM FOR COASTAL AQUACULTURE

USING ARTIFICIAL NEURAL NETWORK AND

GEOGRAPHICAL INFORMATION SYSTEMS

Case Study: Mahakam Delta, East Kalimantan

I KETUT SUTARGA

GRADUATE SCHOOL

(12)

ABSTRACT

(13)

(14)

DEVELOPMENT OF LAND SUITABILITY EVALUATION

SYSTEM FOR COASTAL AQUACULTURE USING

ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL

INFORMATION SYSTEMS

Case Study: Mahakam Delta - East Kalimantan

I KETUT SUTARGA

A Thesis submitted for the degree of Master of Science

of Bogor Agricultural University

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY

FOR NATURAL RESOURCES MANAGEMENT

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

(15)

Student ID : G.051030131

Study Program : Master of Science in Information Technology for Natural Resources Management

Approved by, Advisory Board:

Dr. Ir. I Wayan Astika, M.Si Supervisor

Dr. Antonius Bambang Wijanarto Co-supervisor

Endorsed by,

(16)

STATEMENT

I, I Ketut Sutarga, hereby stated that this thesis entitled:

Development of Land Suitability Evaluation System for Coastal Aquaculture Using Artificial Neural Network and Geographical Information Systems

(Case Study: Mahakam Delta, East Kalimantan)

Bogor, November 2005

(17)

ACKNOWLEDGEMENT

I would to extend my special thanks to the following people for their contribution to this thesis.

ϖ To my external examiner Dr. Ir. Djoko Purwanto, DEA, for his corrections, suggestions and positive ideas of this thesis.

ϖ To the MIT-SEAMEO BIOTROP management and staffs, as well as IPB Post Graduate who have supported our administration, technical aspects and the facilities.

ϖ To the lecturers of MIT who taught me with important knowledge for my future prospects.

ϖ To Bakosurtanal, especially for chairman and the head of the Center for Marine Natural Resource Survey for giving me the opportunity and funding to study for higher education.

(18)

(19)

STATEMENT ... I

ACKNOWLEDGEMENT... ii

CURRICULUM VITAE ... iii

ABSTRACT ...iv

TABLE OF CONTENTS ...v

LIST OF TABLES ...vi

LIST OF FIGURES ...vii

LIST OF APPENDICES ... viii

I. INTRODUCTION ...1

1.1. Background ...1

1.2. Objectives ...4

1.3. Problem Statement ...5

1.4. Research Output ...5

II. LITERATURE REVIEW ...7

2.1. Geographical Information Systems ...7

2.1.1. Spatial and Attribute Data ...7

2.1.2. Spatial Analysis ...9

2.1.2.1. Vector Data Analysis ...10

2.1.2.2. Raster Data Analysis ...11

2.2. Artificial Neural Network ...12

2.2.1. Artificial Neural Network Basics ...12

2.2.2. Multi Layer Feedforward-Backpropagation ...15

2.2.2.1. Backpropagation Model ...15

2.2.2.2. Backpropagation Learning Algorithm ...16

2.3. Land Suitability ...19

2.4. Coastal-Aquaculture ...21

………... 21

2.1.1. Aquaculture System ………21

2.1.2. Cultivable Species ……….…… 22

2.5. Previous Related Research ……… 23

III. RESEARCH METHODOLOGY ………25

(20)

3.3.1. Data Acquisition ……… 26

3.3.2. Field Data Processing ……… 28

3.3.3. Equipments ……… 29

3.4. Procedures ………. 30

3.4.1. Spatial Database Preparation ……… 30

3.4.2. Mapping Suitability by Vector Analysis ……….. 32

3.4.3. Map Conversion and Combination ……….. 34

3.4.4. Building Artificial Neural Network ………. 35

3.4.4.1. Training Process ……….. 35

3.4.4.2. Validation ……… 37

3.4.4.3. Map the Raster GIS and ANN Suitability …….. 38

3.5. Land Suitability Comparison ……… 40

IV. RESULT AND DISCUSSION ……….. 41

4.1. Performances of ANN and GIS Method ……… 41

4.1.1. Training Data Sets ………. 41

4.1.2. Validation Data Sets ……….. 42

4.2. Land Suitability Map of Raster GIS and ANN ………. 43

4.3. Land Suitability Analysis using Vector-Map Overlay ……….. 45

4.4. Comparison of Field Data and Land Suitability ……… 48

4.5. Comparison of The Two Methods ……… 50

4.5.1. Suitability Distributions ………. 50

4.5.2. Area Covers ……… ……….. 52

V. CONCLUSIONS ……… 56

5.1. Conclusions ……… 56

5.2. Recommendations ………. 57

REFERENCES ……….. 58

(21)

LIST OF TABLES

No. Caption Page

1.1. Land cover change of Mahakam delta: 1980-1996 ……… 4 3.1. Map and data that used in the research ………. 29 3.2. Classification scheme on aquaculture suitability, especially for

shrimps and crabs ……….………. ₃₁

3.3. Range of suitability in aggregate of vector spatial analysis ………….. 34 3.4. Example of input (X) and output (Y) pattern of S1, S2, S3, and N ..… 37 4.1. Accuracy of ANN for training dataset processing ……… 42 4.2. Accuracy of ANN for validation dataset processing ……… 43 4.3. The “Unclassified” numbers of the prediction dataset ………. 44

4.4.

Comparison between GIS raster together

with ANN and GIS vector map overlay

………

(22)

LIST OF FIGURES

No. Caption Page

2.1. Overlay of map layer to generate the aggregate new output …………. 10 2.2. The simplified configuration of an organic neuron (a), and artificial

neural network (b) ………... ₁₃

2.3. The sigmoid activation function ……….. 14

2.4. Backpropagation neural network ……… 16

3.1. The map of study area ……… 25

3.2. Distribution of water characteristic point observation, from field survey (2005) and obtained from Total Indonesia

(2004-2005)……… … ₂₇

3.3.

_{Process of GIS map overlay steps of vector}

spatial analysis ………..

32 3.4 Structure of multilayer feedforward backpropagation ANN ……….. 35 3.5 Process of GIS raster with training and validation of ANN ………… 39 3.6 Predicting the land suitability of GIS raster with ANN ……….. 40 4.1. The relationship of accuracy achieved and iteration epoch number .. 41 4.2. Land suitability map using raster GIS and ANN analysis ………….. 46 4.3. Land suitability map using vector map overlay analysis ………… ... 47 4.4. Distribution of pond productivity that produce shrimp and crab in

kg/ha/year ……… ₄₉

4.5. Distribution of the suitability differences between GIS vector spatial

analysis and GIS raster with Artificial Neural Network .……… ₅₁ 4.5. Comparison of area resulted (in ha) of both methods ………. 52

(23)

LIST OF APPENDICES

No. Caption Page

1. The training dataset ………. 61

2. The validation dataset ………. 62

3. The ANN prediction of suitability classes

4. List of points of water characteristic observation ……….. 68 5. List of shrimp and crab yield in the study area ……….. 70 6. Thematic layer: water salinity map ……… 71 7. Thematic layer: water dissolved oxygen map ……… 72

8. Thematic layer: Water pH map ………. 73

9. Thematic layer: soil map ……… 74

10. Thematic layer: rainfall intensity map ……… 75

11. Thematic layer: land cover map ………. 76

(24)

STATEMENT ... i ACKNOWLEDGEMENT... ii CURRICULUM VITAE ... iii ABSTRACT ...iv TABLE OF CONTENTS ...v LIST OF TABLES ...vi LIST OF FIGURES ...vii LIST OF APPENDICES ... viii I. INTRODUCTION ...1 1.1. Background ...1 1.2. Objectives ...4 1.3. Problem Statement ...5 1.4. Research Output ...5 II. LITERATURE REVIEW ...7 2.1. Geographical Information Systems ...7 2.1.1. Spatial and Attribute Data ...7 2.1.2. Spatial Analysis ...9 2.1.2.1. Vector Data Analysis ...10 2.1.2.2. Raster Data Analysis ...11 2.2. Artificial Neural Network ...12 2.2.1. Artificial Neural Network Basics ...12 2.2.2. Multi Layer Feedforward-Backpropagation ...15 2.2.2.1. Backpropagation Model ...15 2.2.2.2. Backpropagation Learning Algorithm ...16 2.3. Land Suitability ...19 2.4. Coastal-Aquaculture ...21 2.1.1. Aquaculture System ...21 2.1.2. Cultivable Species ...22 2.5. Previous Related Research ...23 III. RESEARCH METHODOLOGY ...25 3.1. Site Descriptions ...25 3.2. Time and Location ...25 3.3. Data and Equipment ...26

3.3.1. Data Acquisition ...26 3.3.2. Field Data Processing ...28 3.3.3. Equipments ...29 3.4. Procedures ...30 3.4.1. Spatial Database Preparation ...30 3.4.2. Mapping Suitability by Vector Analysis ...32

(25)

3.4.3. Map Conversion and Combination ...34 3.4.4. Building Artificial Neural Network ...35 3.4.4.1. Training Process ... 35 3.4.4.2. Validation ...37 3.4.4.3. Map the Raster GIS and ANN Suitability ...38 3.5. Land Suitability Comparison ...40 IV. RESULT AND DISCUSSION ...41 4.1. Performances of ANN and GIS Method ...41 4.1.1. Training Data Sets ...41 4.1.2. Validation ...42 4.2. Land Suitability Map of Raster GIS and ANN ...43 4.3. Land Suitability Analysis using Vector-Map Overlay ...45 4.4. Comparison of Field Data and Land Suitability ...48 4.5. Comparison of The Two Methods ...50 4.5.1. Suitability Distributions ...52 4.5.2. Area Covers ...52 V. CONCLUSIONS ...55 5.1. Conclusions ...55 5.2. Recommendations ...56 REFERENCES ...57 APPENDICES ...59

(26)

LIST OF TABLES

No. Caption Page 1.1. Land cover change of Mahakam delta: 1980-1996 ...4 3.1. Map and data that used in the research ...29 3.2. Classification scheme on aquaculture suitability, especially for shrimps

and crabs 31

3.3. Range of suitability in aggregate of vector spatial analysis ...34 3.4. Example of input (X) and output (Y) pattern of S1, S2, S3, and N ...37 4.1. Accuracy of ANN for training dataset processing ...42 4.2. Accuracy of ANN for validation dataset processing ...43 4.3. The “Unclassified” numbers of the prediction dataset ...44

4.4. Comparison between GIS raster together with ANN and GIS vector map overlay 54

(27)

LIST OF FIGURES

No. Caption Page

2.1. Overlay of map layer to generate the aggregate new output ...10 2.2. The simplified configuration of an organic neuron (a), and artificial

neural network (b) 13

2.3. The sigmoid activation function 14 2.4. Backpropagation neural network 16 3.1. The map of study area 25

3.2. Distribution of water characteristic point observation, from field survey (2005) and obtained from Total Indonesia (2004-2005) 27

3.3. Process of GIS map overlay steps of vector spatial analysis 32 3.4. Structure of multilayer feedforward backpropagation ANN 35 3.5. Process of GIS raster with training and validation of ANN 39 3.6. Predicting the land suitability of GIS raster with ANN 40

4.1. The relationship of accuracy achieved and iteration epoch number 41 4.2. Land suitability map using raster GIS and ANN analysis 46 4.3. Land suitability map using vector map overlay analysis 47

4.4. Distribution of pond productivity that produce shrimp and crab in kg/ha/year 49

4.5. Distribution of the suitability differences between GIS vector spatial analysis and GIS raster with Artificial Neural Network 51

(28)

LIST OF APPENDICES

No. Caption Page

1. The training dataset ...59 2. The validation dataset ...60 3. The ANN prediction of suitability classes ...61 4. List of points of water characteristic observation ...67 5. List of shrimp and crab yield in the study area ...68 6. Thematic layer: water salinity map ...69 7. Thematic layer: water dissolved oxygen map ...70 8. Thematic layer: Water pH map ...71 9. Thematic layer: soil map ...72 10. Thematic layer: rainfall intensity map ...73 11. Thematic layer: land cover map ...74 12. Thematic layer: land use plan ...75

(29)

I. INTRODUCTION

1.1. Background

Land evaluation is a process of assessing the land for defining the suitability for specific uses. Generally, land evaluation deals with agriculture, which provides information and recommendations for deciding 'which crops to grow where' and other related questions. The land evaluation in non-agriculture purposes has also been conducted in many applications, such as site selection for specific land allocation for waste disposal, real estate, sport center and others. The main product

of land evaluation investigations is a land classification that indicates the suitability of various kinds of land for specific land uses, and it is usually depicted on maps with accompanying reports. Land suitability

is the selection of suitable land, and suitable cropping and

management alternatives that are physically and financially practicable and economically viable. The land suitability is the aptitude of a given

type of land to support a defined use. The process of land suitability classification is the appraisal and grouping of specific areas of land in

terms of their suitability for a defined use.

Land suitability for certain purposes is needed to be defined to get optimal utilization of land. Government should arrange and specify the

land use to have sustainability of the land and to maintain the utilization. Another important aspect of land suitability is to allocate the

(30)

benefit and positive impact to increase local government revenue and local economy.

Several methods have been used to define land suitability both for agriculture or non- agriculture, such as the conventional method of spatial analysis, multi criteria decision-making, and analytical hierarchy process. This research will attempt to apply the artificial neural network

(ANN), that will be integrated to geographical information systems (GIS). Mahakam Delta of East Kalimantan (Indonesia) area will be evaluated for its land suitability based on physical aspects only using

this method. Land suitability of that area will be defined for coastal aquaculture.

Land suitability is assessed and classified with respect to specified kinds of uses. Evaluation is done through land suitability

involving comparison of two or more alternative kinds of use. The suitability classes are defined by physical criteria, so that the multidisciplinary approach is required. Suitability refers to land use on

a sustained basis. An ideal system to evaluate the land suitability should take into account the physical, economic, social and political

context of the area concerned. However, due to the limitation of Mahakam Delta data availability, time and cost, the evaluation will be

conducted based on physical aspects only.

Spatial analysis represents the methods to do land suitability, which utilizes the GIS functionalities. It is conducted by superimposing one map layer to another to perform the land unit for scoring, summing,

classifying and mapping the suitability classes. The process takes long time to achieve the land suitability class map, because it conducts the

(31)

To overcome the conventional GIS-spatial analysis limitations, the automatic spatial analysis is then introduced by integrating GIS technology with neural network. Neural network represents the new

method, based on simplified models of the human-central nervous system (Peterson, 1996). This method has been applied in many fields such as: constraint satisfaction, forecasting, general mapping, control,

data compression, diagnostics, optimization, pattern recognition, risk assessment, and multi sensor data fusion. In this research, neural network will be applied to classify land suitability based on GIS-spatial

analysis data. This research aims at defining the land suitability for aquaculture in Mahakam Delta-East Kalimantan, Indonesia by evaluating the neural network method on GIS raster data and by

comparing the results against vector map overlay.

The developments of Mahakam Delta are very dynamic that can be indicated on the series of land cover area recorded by the ‘PT. Total Indonesia’ company (Total Indonesia, 2001) as shown in Table 1.1. The

table describes the trend of land cover change from 1980-1996. The

most rapid change area is Ponds (fish/shrimp ponds) which is 0 km

square in 1980 into 153.67 km squares in 1996. Other significant land cover changes are: degraded forest, deltaic culture, dense avicenia, nypa and dispersed avicenia, and pure nypa. This indicates that there is a significant land utilization change from Non-Ponds into Ponds. Due to

this uncontrolled land cover change trend of the Mahakam Delta, special attention is needed. Local government should facilitate the land

(32)

trend of changing from non-ponds into ponds problems in this study area.

Table 1.1. Land cover changes of Mahakam Delta 1980-1996. Area (km2₎

N o. Land Cover

1980 1986 1992 1996 1. Cultivated plain 137.009582 135.824139 135.411144 130.847731 2. Degraded forest 50.504413 49.905991 48.942083 33.603932 3. Degraded marsh 0.446872 0.446872 0.446872 0.202236 4. Deltaic culture 25.066147 25.066147 12.312481 8.777911 5. Dense Avicenia 83.212181 82.511241 75.975907 59.613158 6. Dense Rhizophora 6.903044 6.903044 6.903044 6.329179 7. Fresh-water mangrove 182.830777 182.830777 182.830777 181.944749 8. Hilly area 49.905819 49.905819 49.905819 49.636616 9. Mixed fresh-water forest 42.479087 42.479087 42.479087 42.479087 10. Non-classified 497.011041 497.011041 492.118834 488.526098 11. Nypa and dispersed Avicenia 77.040897 75.619431 73.826446 54.415846 12. Nypa and Rhizophora 6.883389 6.883389 6.883389 6.476434 13. Pure Nypa 580.612391 580.323536 575.163949 523.801519 14. Sea 2236.360907 2236.360907 2236.36091 2236.360907 15. Sonneratia 1.376154 1.376154 1.376154 1.376154

16. Ponds 0 4.195151 36.705801 153.666758

17. Tidal flat 24.293085 24.293085 24.293085 24.293085

18. Yard 2.159298 2.159298 2.159298 2.159298

1.2. Objectives

The objectives of this research are:

1) To develop a land evaluation system on coastal aquaculture suitability by using artificial neural network (ANN) and geographical information system (GIS).

2) To evaluate the performance of the new developed system as compared to the GIS-vector spatial analysis method. The comparison covers its suitability distribution, covered area, Source: Mahakam Delta landcover map series, 2001 by PT. Total Indonesia

(33)

processing speed and validity by taking coastal aquaculture suitability at Mahakam Delta-East as a case study.

1.3. Problem Statement

As mention earlier, land evaluation is used to define the land suitability for specific purpose such as coastal aquaculture in Mahakam

Delta, East Kalimantan. This suitability classes are needed for arranging and specifying within this under developing area because of

the uncontrolled land use change. This area represents the most dynamic area where the land is increasing in significant amount caused

by eroded process in upper land and heaped in the coastal. So, this is why the land uses for spatial planning is needed to be emphasized in

more detail.

ANN is commonly used in image processing for supervised classification method of remotely sensed data. In this case, a research

will be conducted on the ‘integrated ANN and GIS’ based on GIS data for defining the land suitability for coastal aquaculture. The land suitability that will be considered is based only on the physical aspects.

Another aspect that need to be emphasized in this research is to develop land evaluation system in the form of application software

based on ANN and GIS 1.4. Research Output

(34)

with ANN classifier. A raster spatial analysis feature in GIS is implemented for overlaying thematic map layer into new merging map,

while land suitability will be classified by using ANN. On the other hand, vector spatial analysis in that study area will also be conducted. This will provide good comparison on the performances of the GIS map

overlay against the proposed new method of ANN to produce land suitability map for aquaculture.

The expected benefit of this research is that using the ANN method, the land suitability map of coastal aquaculture can be done

more effectively and efficiently. This is not only for the case of Mahakam Delta, but also for land suitability analysis in general. In the

case of Mahakam Delta, Coastal aquaculture suitability, will be assessed only for the brackish water farming in the land, such as tiger

shrimp (Penaeus monodon) ponds even mud crab (Scylla serrata). The

land suitability on aquaculture availability would facilitate the local government to make a decision on Mahakam Delta management. In the

long term, it will be expected the method will provide good assistance in managing the area sustainably.

(35)

II. LITERATURE REVIEW

2.1. Geographical Information Systems

2.1.1. Spatial and Attribute Data

A geographical information system (GIS) is a computer-based system for managing spatial data, including the functionalities for data

capture, input, manipulation, transformation, visualization, combination, query, analysis, modeling and output. (Bonham-Carter,

1994). GIS functionalities are the reliable tool for spatial analysis dealing with geo-referenced data. Geographic data are characterized by

two fundamental components: the phenomenon such attribute and the spatial location of it. Geographic data are inherent form of spatial data. Geo-referenced data mean spatial data that pertain to location on the

earth’s surface.

Spatial data are associated with map, whereas attributes data are supported with tables. The representation of spatial data is crucial for any further processing and understanding of data. In general, spatial

data model consists of raster and vector models.

• Raster data model is a regularly spaced set of cells with associated

field values. The location of geographic objects is defined by its row and column position of the cell they occupy. The associated value with each cells are not illustrated, they are individual cells. Each cell is stored in a unique value and represents an area of the land surface.

(36)

• Vector data model represents the geographic objects in the real world using the points and lines that define their boundaries. Their locations on earth’s surface are referenced to map position using Cartesian coordinate system. Points, lines and polygons are used to represent irregularly distributed geographic object. The spatial entities in vector model of correspond more or less to the spatial entities that they represent in the real world.

An advantage of vector data model is efficient representation of topology. However, it s a complex data structure, difficult to do overlay

and inefficient for image processing. On the other hand, raster data model can make simple data structure, simple to do overlays and efficient for image processing, although raster format is less compact

data structure and difficult to represent topology. Raster data format enables to conduct map algebra such as for map combination. Another

advantage of raster data model is that raster provides wider variety of range of attribute data, because each cells has value it self. Based on

this consideration, raster data model is chosen in this research incorporated with ANN.

One important thing of GIS software is spatial data organization. A spatial data layer is either a representation of continuous or discrete field, or a collection of objects of the same kind. A data layer contains

spatial data of any attribute (or thematic) data. Data layers can be overlaid with each other in GIS software. Spatial data layers can perform spatial analysis such as overlay function, map algebra or other

GIS analysis functionalities.

Database management systems are computer systems for handling any kind of digital data. Database management systems are

(37)

the heart of any GIS. GIS system allows storing and manipulating the database. Some GIS software use internal DBMS to manage non-spatial

data such attribute or thematic information, others provide linkages to external DBMS.

Attribute is descriptive information characterizing a geographical feature (point, line and area). Fact describes an entity in a relational

data model equivalent to the column in a relational table. Attribute values can be written based on primary observation or they can be derived by secondary processing and calculation. Attribute of spatial

object are usually organized into lists or tables. It draws in form of computer organizational tables, two-dimensional arrays of number with

rows being entities or object and columns being attributes. Attribute is also known as thematic information, which refers to other kinds of properties of geographical object, such as land use, annual rainfall,

rock type or soil type.

Each of polygon, which is coded by value of features relates to each attribute in each record. Numeric codes are the unique value that

can be distinguished from others. The relationship of spatial and attribute data that describing the geographical information could be

formed both in vector and raster data model. This geographic information format is simply depicted in the GIS software.

2.1.2. Spatial Analysis

The principal objective of spatial analysis is to transform and combine data from diverse source into useful information, to improve

(38)

suitability for certain purpose, modeling and many other applications. Spatial analysis, in this case, will be conducted both in vector and

raster data format.

2.1.2.1. Vector Data Analysis

Spatial analysis of vector data is conducted by utilizing the GIS functionality in map overlay. Many functionalities of map overlay, i.e.

union, intersect, identity, erase. Map overlay functions combine the geometry and attributes of two polygon feature maps to create a new

output map. Most of map overlay will be used by applying “Union’ operation. The illustration of ‘Union’ operation is depicted in Figure 2.1.

The output map represents the aggregate of the input both in spatial and attributes. Map overlay of several feature maps (layers) is destined

only for polygon features.

For map layer consisting of line features such as rivers and pipe gas line, analysis can be done by applying a zoning of it. This can be done by applying “Buffer functionality. Buffering is the creation of

polygon features as far as a distance from the line center.

Layer -1 Layer -2

. . . . . .

Layer -n

Input Layers Output Layer

(39)

The most important thing in map overlay is the attribute of each layer database. Attribute of each map layer contains thematic information and its score, respectively. Attribute distinguishes one

feature to others, while score determines the numeric value of attributes. Both of attribute and score of each layer are brought to the output map layer. The output map layer represents the aggregate of all input layer. Analysis of new features of output map corresponds to the total score of numeric value. Classification of total score represents

feature class of output map. In this research, classification of total score aggregate is conducted by implementing the equal interval of the

maximum and the minimum range of values. 2.1.2.2. Raster Data Analysis

As mention before, the raster data model uses a regular grid to cover the space in each grid cell to correspond to the characteristic of a

spatial phenomenon at the cell location. Raster data analysis can be performed at the individual cell, or group of cell. An important consideration in raster data analysis is the type of cell value and cell size. Cell size deals with the detail level of information to be created. Smaller cell size indicates more detail information and vice versa.

Raster analysis begins with the set up of an analysis environment including the area extent and cell size. The extents zone of raster grid

(40)

Joining raster layer is implemented using cell-by-cell operations (or local operation). A local operation creates a new grid from either a single input grid or multiple input grids, and the cell values of the new grid are computed by a function relating the input to the output. Joining

process in raster grid data looks like Union operation in vector format. However, joining process is simpler and faster rather than union

operation. Analysis of raster data is incorporated with ANN. 2.2. Artificial Neural Network

2.2.1. Artificial Neural Network Basics

Artificial Neural Network represents the computational through parallel-distributed information structure consisting of a set of adaptive

processing (computational) elements and a set of unidirectional data connections (Abrahat and Fischer (2000). These data connection (or

network) are “neural” in the sense that they have been inspired by neuroscience as biological or cognitive neural phenomena. In fact, ANN

is more common with traditional mathematical and/or statistical model, such as non-parametric pattern classifier, statistical regression model

and clustering algorithm, rather than neurobiological models. The simplest neural network architecture is a single layer feedforward network. It is a single layer because the input patterns are

processed through a single layer of neuron only. An input pattern is propagated through neural synaptic weight connections to the neuron where response is generated as the output activation. It is feedforward because signals propagate only in a forward direction, from the input

nodes to the output node. The simplified configuration of organic neuron and its relation to the artificial model of neural network are

(41)

The basic computing element of biological system is the neuron. A neuron is a small cell that receives electrochemical stimuli from multiple sources and responds by generating electrical impulse that are

transmitted to other neurons or effector’s cells. Due to the amount of

neurons that will be processed, the architecture of artificial neural

network is fully done by a large number of nodes and connections.

Each connection points from one node to another is associated with a

weight. According to Fu (1994), the artificial neural network should

involve the following tasks: determination of network properties, node

properties and learning algorithm.

The network properties include connectivity (topology), type of

connections, the order of connections, and the weight range. The

topology of a neural network refers to its framework as well as its Figure 2.2. Simplified configuration of an organic neuron (a), Artificial model of neuron (b), (Veelenturf, 1995).

S om a

D endrites

A xon _{S ynapses}

x 1

x 2

x 3

x n

w 1 w 2 w 3 w n

(42)

by the number of layers and the number of nodes per layer. Three types of layers include (Fu, 1994):

• The input layer: The nodes, which encode the instance presented to the network for processing. For example, each input unit may be designated by an attribute value possessed by the instance.

• The hidden layer: The nodes, which are not directly observable and hence hidden. They provide nonlinearities for the network.

• The output layer: The nodes which encode possible concept (or

value) to be assigned to the instance under consideration. For example, each input unit represents a class of object.

Node properties of a neural network is the transfer function to be done corresponding to the activation level. The activation levels of nodes can be discrete (e.g, 0 and 1) or continuous across a range (e.g., [0,1]) or unrestricted. This depends on the activation (transfer) function chosen. If it is hard limiting function, then the activation levels are 0 (or

–1) and 1. For sigmoid function, the activation levels are limited to a continuous range of real [0,1]. The sigmoid function F (Fu, 1994) is formulated in equation 1, while Figure 2.3 illustrated the bonded value

of that function.

e x

y ₋

+ =

1 1 ) (

……….1)

Figure 2.3. The sigmoid activation function (Fu, 1994) 1.0

0.5 O utput

Input 0

(43)

The learning rule is one of the most important attributes to specify for a neural network (Fu, 1994). The learning rule determines how to adapt connection weights order to optimize the network performance. It

indicates how to calculate the weight adjustment during each training cycle. The weight initialization scheme is specific to the particular neural network model chosen. In many case, initial weights are just

randomized to small real numbers.

The basic performance of ANN is to learn the relation between input and output, then to accommodate for getting consistent responses. Efficient learning algorithms should be developed, particularly for the network with multiple layers and large number of interconnection. Computation is done by iterative learning procedures

to obtain an adequate weight value. Learning method is suited to the ANN model and mathematical model. Methods of learning can be

categorized into supervised, reinforced and unsupervised. This research chooses a supervised method that applies the

backpropagation algorithm for learning process. 2.2.2. Multi Layer Feedforward-Backpropagation

2.2.2.1. Backpropagation Model

Backpropagation is a learning algorithm using multilayer feedforward network with a different transfer function in artificial neuron. Backpropagation learning algorithm is usually implemented in

multi (three or more) layer neural network. This learning algorithm accommodates both real and integer numbers for input and output. The

(44)

output layer. The hidden layer only receive internal inputs (inputs from other processing units) and hidden from outside world.

The backpropagation arises from the method in which corrections are made to the weights (Patterson, 1996). During the learning phase, input patterns are presented to the network in some sequences. Each training pattern is propagated forward layer by layer until an output

pattern is computed. The computed output is then compared to a desired value or target output and error value is determined. The errors

are used as inputs to feedback connections from which adjustments are made to the synaptic weights layer by layer in back direction. The

backward linkages are used for the learning phase, whereas the forward connections are used for both the learning and the operational

phase.

The processes inside the feedforward backpropagation algorithm network are described in the Figure 2.4.

2.2.2.2. Backpropagation Learning Algorithm

Figure 2.4. Backpropagation neural

xi : input variable of node i in input layer

hj : output of node j in hidden

layer

yk : output of node k in output

layer (predicted value of node k)

wij : weights connecting node i in

input layer and node j in hidden layer

vjk : weights coonecting node j in

hidden layer and node k in output layer. x 0 x 1 x i h 0 h 1 h 2 h 3 h j y1

IN P U T L A Y E R

H ID D E N L A Y E R

O U T P U T L A Y E R

yk w ij

(45)

In ‘the backpropagation learning algorithm’ the training instance

set for the network must be presented many times in order for the

interconnection weight between the neurons to settle into a state for

correct classification of input pattern. The basic learning algorithm of

back propagation modifies the interconnection weight on the network

so that signal error is minimum (closer to zero).

Figure 2.4 shows a backpropagation neural network with one

hidden layer and full interconnection. Refers to Patterson (1996), the

notations of network parameters are:

v

: Weight connections between input layer unit i and hidden layer unit j

i = 1,2,…,N, j = 1,2,…,H

w

: Weight connections between hidden layer unit j and output layer unit k

j = 1,2,…,H, k = 1,2,…,M

x

: Input training pattern, p = 1,2,…P

p j

y

: Output hidden layer unit j for input pattern unit

x

p k

z

: Output from unit k of the output layer for input pattern

x

p k

t

: Desire or target output

In each hidden layer, the net input that represent the sum of input

nodes times weight can be computed as: _ij _i

w

x

H

=

Σ

and

j jk j

v

h

I

=

Σ

, where

H

_j is net input of input layer-hidden layer unit j,

and _I_k is the net input to unit k of the output layer, respectively.

(46)

a. Normalization of input data xi and target tk in form of (0, 1) range

b. Randomize of weight wij and vjk using (–1, 1) value. c. Initialize of thresholding unit activation, x0=1 and h0=1. 2. Activate of input layer-hidden layer units with:

i ijx w j

e

h

_Σ

+

=

1 ………....… 2)

3. Activate of hidden layer-output layer units with:

j jkh v k

e

y

_Σ

+

=

1

………. 3)

4. To minimize error of weight, vjk must be adjusted. This

process is called ‘backward’ step. Adjustment of vjk is done by

computing error of the nodes in output layer, denotes

δ

_kthen adjusts weight vjk:.

)

)(

1 (

k k k

=

−

y

t

−

y

δ

... 4)

j k jk

v

h

v

=

+

β

.

δ

.

... 5)

where: ββ is constant of momentum ,

tk is prediction value.

5. Similar to step 4, the nodes can be backward stepped in

hidden layer to adjust wij:

jk k k j j

h

(

1 h

)

δ

.

v

τ

=

−

Σ

... 6)

i k ij

w

x

(47)

6. Update all weight. Refining weight is needed when output has significant difference from input. To minimize error, each layer is refined using delta rule (Patterson, 1996):

j i

h

w

=

α

.

δ

.

∆

... 8)

k j

x

v

=

α

.

δ

.

∆

... 9)

where αα is learning speed constant

So, the refinement weight:

ij old

ij new

w

=

+

∆

... 10)

jk old

jk new

v

=

+

∆

... 11)

7. Return to step 2 and repeat each pattern p until the total error has reached an acceptable level (iterative). Iteration process is done to achieve the minimum error using the refinement weight. The performance of neural network can be evaluated based on the value of root mean square error (RMSE), using:

n t h

RMSError k k

2 ) ( − Σ =

……….. 12)

The trained neural network can be used to predict target (T) by inputting values from input layer (X).

2.3. Land Suitability

Land evaluation is the process of assessing of land performance when (the land is) used for specified purposes (FAO, 1985). The land is the ultimate source of wealth and the foundation on which civilization is

(48)

appropriate and sustainable use of natural and human resources. Land suitability represents a method of land evaluation.

Land suitability analysis estimates which areas are suitable or not suitable for certain development. The land suitability can be determined by using matching methods between land suitability criteria and land characteristics. The process of land suitability classification is the appraisal and grouping of specific areas of land in terms of their suitability for a defined use. The suitability is the aptitude of a given type of land to support a defined use.

To produce the land suitability, two concept of land evaluation are known, i.e. physiographic approach and parametric approach. Physiographic approach utilizes landform framework to identify the natural land unit, while parametric approach divides the land following the distinguish land value and its combination. Parametric approach is

more suitable for this research, due to all of parameters that are quantized. In this study two categories are recognized: orders and classes. The orders indicate whether or not given types of land are suitable for the concerned land utilizations type and are expressed by

the symbols S and N (FAO, 1976):

S (suitable) : Land on which sustained use is expected to yield

benefits which justify the inputs, without unacceptable risk of damage to land resources N (not

suitable)

: Land whose qualities appear to preclude

sustainability for the considered land use Classes reflect degrees of suitability within the order “suitable”.

(49)

S1 (highly suitable)

: Land which has no significant or only minor

limitations to the sustained application of the given land utilization

(moderately suitable)

: Land which has limitations that are moderately

severe for sustained application of the given land utilization. The limitations will reduce productivity or benefits and will increase the required inputs S3

(marginally suitable)

: Land which has severe limitations for sustained

application of the land utilization

Because it is based only on physical aspect of

suitability orders, there will be no differentiation

between N1 and N2. In this case, the ‘not suitability

’

of land for coastal aquaculture will be assumed as N

(not suitable).

N (not suitable)

: The limitations are so severe that they preclude the successful application of the given land utilization

2.4. Coastal-Aquaculture 2.4.1. Aquaculture System

Aquaculture is the farming of aquatic organisms, including fish,

molluscs, crustaceans and aquatic plants. The objectives of farming of

aquatic organism are to have control over their growth and propagation

or breeding by judicious rearing of these organisms. Rearing is

intended either increasing the quantity or improving the quality of

product, and make the process economically so that the farmers get

some profit.

(50)

(Scylla serrata). Shellfish is not true fish, it includes: crustaceans class (shrimps, lobster, crab) and molluscs class (oyster, clam, mussel, scallops, abalones, conch, squid). While Fin fish is true fish, includes:

mullets, milk fish, yellow-tail eel, pearlspot, red sea bream, grouper, tilapia, plaice, salmon, pompano, etc (Bose, 1991)

The aquaculture system is distinguished to the level of intervention applied. These three culture techniques can be applied to increase the productivity of aquaculture system, i.e.: extensive,

semi-intensive and semi-intensive (Furey and Pitman, 2003). Extensive system applied with low cost involved and minimum requirement of management. The simplest form of extensive system is the natural

system, where it only utilizes the natural requirement. Types of extensive system are low input, low capital and operating cost and low

level of management. Another aquaculture system such as semi intensive and intensive is carried out by higher level of

intervention-applied, includes: input, cost, management aspects. 2.4.2. Cultivable Species

Based on the brackish water environment, these tiger shrimps and crabs species survive with wide range of temperature and salinity. The

most consideration is economics value, which represents the mean commodity of fish (or shell fish) commerce. Requirement of shrimp and

crab culture should involve some physical aspect such as: soil characteristics, topographical, climate, hydrology and water quality (Hardjowigeno et al, 1996). Kapetsky and Nath (1997) added and enrich

those criteria with support and input aspects, risk and natural indicators. Both researchers use the similar criteria, but the second

(51)

• Shrimps. Shrimps or prawns represent one of food source

containing a high protein. Tiger shrimp (Penaeus monodon)

represent the important export commodity besides the petroleum.

Consumer request to mean prawn go up 11,5% per year (

Ministry

of Research and Technology

, 2005), therefore, this commodity represents the most species being cultured in Indonesia. The body can attain the maximum size of 27 cm in length and 130 gram in weight, however, the commercial size being above 12 to 13 cm in body length and 20 gram in body weight. Culture of tiger shrimp in brackish water very much depend on the technology aquaculture system to be applied. The suitable water salinity for shrimp is 12 to 20 ppt.

• Crabs. The mangrove crabs (Scylla serrata), have long been an

incidental product of brackish water pond culture, and sometimes even deliberately stocked in fishponds. The market of this commodity is increasing, both for domestic and export needs. This matter can not fulfill for natural arresting from brackish water only; however, it is a challenge to meet the market through farming system (Ministry of Fisheries and Marine, 2003). Commercial size of crab body is 10 cm in length. Ponds of cultivated crab should be 0.8 to 1.0 meters deep, and the suitable water salinity is 15 – 30 ppt. Texture of pond soils is silty loam or sandy loam. The preferred tidal difference for crab live is ranging from 1.5 – 2.0 meters.

(52)

Poly-culture can be applied in extensive aquaculture system, which utilizes the natural feed of brackish water environment.

2.5. Previous Related-Researches

Site selection for development of shrimp and mud crab culture have been assessed by Salam and Ross (1997), in Southwestern of Bangladesh. Their models were developed using remote sensing and GIS tool. Remote sensing data was used for obtaining water body, GIS

database incorporated the existing environmental layer such waters, soil, land use, water temperature, rain fall, salinity and pH. Infrastructure is also included, such as roads, market and processing

plant. From those factors, they developed sub model by defining suitability classes: very suitable, moderately suitable, marginally suitable and presently not suitable. Then, multi criteria evaluation was

applied to determine the weight of each parameter to produce the suitable sites for shrimp and crab farming.

The use of remote sensing technique for shrimp farming site suitable selection was conducted in coastal area of Bangladesh (Islam

et al, 1999). They used various types of data, including: different satellite data, thematic map, field measured and other published information. Six major works are construction of fisheries database,

test of GIS suitable site shrimp farming, implementation of model, application and analysis of output model, socioeconomic assessment

of the site, and analysis of expansion impact and risk. Fishery resources have been successfully analyzed in the areas using GIS

modeling of suitable site selection for shrimp farming.

Easson and Barr (1996) studied the integration of ANN and GIS to interpret natural resource information. The feasibility of the integration

(53)

of these two technologies has been proven in recent research and has potential applications to other forms of geological and hydrological

interpretations.

Land evaluation expert systems, which is an automated land evaluation system with the transformation of land suitability classification expertise, following the FAO framework, into trained ANN

has been developed by Bandibas (1998). An error back-propagation ANNs model was used. The trained ANNs were stored in a database, representing the knowledge base. This knowledge base was used in a land system successfully to determine the best land suitability class in

(54)

III. RESEARCH METHODOLOGY

3.1. Site Description

Administratively,Mahakam Delta is governed by Kutai Kertanegara

Regency, East Kalimantan Province - Indonesia. This area covers more

than 2000 km squares. Geographically, it is located between 117.320 –

117.610 E, and 0.310-0.920 S. This area represents the downstream of Mahakam Rivers while upper course is in the Center of Borneo Island,

as shown in Figure 3.1.

3.2. Time and Location

The research was carried out in two separate locations. Preparation, field data computation, application of data processing and report writing were conducted in Bogor, West Java starting from

March-August, 2005. Meanwhile, field data acquisition was acquired in Mahakam Delta and the Government of Kutai Kartanegara Regency -

East Kalimantan, from July 24 – 30, 2005.

Mahakam Delta

(55)

3.3. Data and Equipments 3.3.1. Data Acquisition

The main spatial data of Mahakam Delta to be implemented for supporting the research objectives include soil characteristics, water characteristics, climate characteristics, land use/land cover and spatial

plan. Another aspects involved in determining the site suitability are rivers and risk factor. Supporting topographical and hydrological aspects, in this research are very important. However, it is difficult to

distinguish the altitude on map in this area. This is because the area being studied is tidal flat, where altitudes ranges from 0 – 3 meters above the mean sea level. Also, it is difficult to determine which area is covered by tidal zone due to the limitation of 1:50000 map scales, where contour interval is drawn at 12.5 meter. Generally, the intertidal zone range of 2-3 meters above the mean sea level and it amplitudes 1.1 - 2.1

meters is suitable for shrimp ponds development (Purnomo, 1992). Based on the tidal requirement, Mahakam Deltas are inclusive of area

that are suitable for brackish water farming where the ranges of tidal are 2.5 meters.

Most of the data for processing were secondary data, which were taken from previous study and part of them were taken from field survey. Secondary data include soil texture, soil drainage, land use,

spatial plan, gas pipelines and rivers map. That map represents features being necessary to GIS processes.

(56)

observed in the field using ‘water quality checker’ instrument,

including:

• Salinity. Salinity represents the total concentration of dissolved salt in the water that expressed in mg/l or ppt. Intertidal is an area

that represent the mixture of fresh and saline water that perform

the brackish water. Salinity of intertidal may differ between low and

high tides. Previous salinity map was utilized, and the new

observation is conducted to obtain the salinity on point data.

Measurement was held by points on rivers, ponds or channel and

offshore.

Figure 3.2. Distribution of water characteristic point observation, from field survey (2005) and obtained from Total Indonesia (2004-2005)

(57)

• Dissolved Oxygen (DO). DO is one of water variables that is significant for aquaculture. Dissolved of oxygen in the water was influenced by other variable such as temperature, salinity, organic matter and brightness.

• pH. Grade of acidity represents the alkaline intensity of hydrogen

ion of liquid. pH is very significant for brackish water aquaculture, so that its concentration should be well balanced to take care of cultured species

Referring to the suitability to be defined, then shrimp and crab yield of pond in the field were recorded from fisherman. However, these

data of ponds-harvest were taken from previous report that studies the productivity of ponds in Mahakam, it includes: shrimp, milkfish and crabs (Total Indonesia, 2005b). The particular yield of shrimp and crab in kg/ha/year of ponds will be used as comparator of the suitability map

which will be produced by GIS model. 3.3.2. Field Data Processing

Some of data obtained from field should be processed so that it will be ready for next GIS and ANN processing, and these include water

salinity, water dissolved oxygen, water pH and rainfall intensity. Water characteristics such as salinity, pH and dissolved oxygen need to be computed and interpolated before drawn on reference map. Also, the rainfall data in this area of 1994-2004 periods are necessary to compute for getting the annual rainfall, and then interpolate the intensity to make

(58)

point data represents the matured data of field that observed using water checker tool. For interpolation processes of all observed field data, the kriging features in GIS functionalities were applied to create

isohyets map, including salinity, dissolved oxygen, pH of water characteristic and the rainfall intensity. The thematic maps, which were

resulted from field data processing, can be shown in the Appendices 6 to 12.

Both primary and secondary data sources and their edition, which will be used in the GIS raster together with ANN and GIS vector map

overlay analysis are described in the Table 3.1. Table 3.1. Map and data used in the research

No. Map/Data

Edition Sources

1. Water Salinity, DO

and pH

2005

Field survey

2. Rainfall data

1994-2004

Agricultural Office of

Kutai Kartanegara

Regency

3. Topography

1991

National Coordinating

Agency for Surveys and

Mapping (Bakosurtanal)

4. Soil texture, drainage

2000

Center of Soils and

Agro Climate Research

(Puslitanak)

5. Land Cover

2001

PT. Total Indonesia

6. Pipeline Distribution

2001

PT. Total Indonesia

7. Spatial Plan (Land

Use Planning)

2003

Regional Plan and

Development Office of

Kutai Kartanegara

Regency

3.3.3. Equipment

Equipment for this research includes field survey and laboratory tools. For field survey the global positioning system (GPS) and water quality checker were used. GPS was used to determine the position on

(1)

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000

Area covers (in ha)

N S2 Unclassified

GIS Raster +ANN GIS Vector Map Overlay

4.5.2. Area Covers

The resulting suitability maps produced using the GIS raster with ANN and GIS vector overlay can be compared, where each suitability class of both methods describe the areas and the distribution. It was found that there was difference in area coverage of both methods (Figure 4.6). This was mainly caused by the fact that the vector map overlay was fully conducted directly for a features and layers, and without predicting the target, while the ANN method uses only several datasets as the training area to estimate or predict the whole classes.

It was also found that the total classified or unclassified areas produced were different (Figure 4.6). The total area of GIS raster together with ANN was 186,666 ha, while the GIS vector overlay was 190,611 ha. This difference was caused by the different data format (raster versus vector), resulting from the vector to raster conversion, where the grid size was 30 meters. Improving the grid size will reduce the difference but requires higher memory storage. The difference areas of suitability were caused by the ANN classifier, which used the supervised method.

The speed of the GIS vector map-overlay was processed lower. It was caused by decreasingly speed of overlay steps. For first step of overlay processing the speed about an hour. The speed will be longer due to increase the number of layer to be

(2)

overlaid, which was increased for 30 minutes in every step. For ten vector layers (as described in Figure 3.3) which done in 9 overlay steps were consumed more than 27 hours. On the contrary, GIS-raster do overlay at once for ten-layers map for about 30 minutes. The GIS-raster combine layers using “Combine” function is faster than “Union” function of GIS -vector map overlay.

Classification of GIS vector also was conducted step by step. The first step was summed all the scores of ten parameters. Next steps, retrieving and classifying of each record were conducted by referring Table 3.3, which classified into four suitability classes S1, S2, S3, and N. The other way, GIS raster classes suitability was done by running the ANN program in the MS Visual Basic 6.0. Training, validation, and classification process were conducted about 5 minutes.

When comparing the distribution of each suitability of both methods, they are located on the same places in the study area. For example, the S1 are located beyond of green belt on GIS raster with ANN method and also in the Vector Spatial Analysis method. For S2, S3, N also place on the same in both map results. In general, both methods produce the suitability classes’ distribution on the same place.

This indicates that the GIS raster accompanying ANN can be achieved the good results for defining the suitability map for aquaculture. It shows by how this method could meet the requirements such as accuracy achievements and class map produced. Using this method data processing will be faster to do data processing due to work on raster grid spatial data. Also it utilizes only a limited dataset, so that, can be efficient in data preparation step.

Base on comparison on how the map was produced, there were several advantages and disadvantages of both methods. Each of methods has distinguished workflow that influent to the final map result. One method may simpler than other. Meanwhile, other method may need full database preparation of thematic layer and

(3)

many primary spatial data as possible. Comparison of GIS raster together with ANN method and GIS vector map overlay is shown in the Table 4.4:

Table 4.4. Comparison between GIS raster together with ANN and GIS vector map overlay.

Items GIS Raster with ANN GIS Vector map overlay

1. Geometric accuracy

Lower, but depends on resolution

Higher

2. Processing speed Faster, because twenty layers can be overlaid at once

Slower, because overlay can only be done for two layers*)

3. Data preparation Necessary for training and needs additional effort.

Can be done for whole layers.

4. Memory storage Depends on the image/layer resolution and number of layers to be analyzed.

Only depends on number of layers, but memory size is usually lower for the same area

*) for analysis of more than 2 layers, the number of overlays are n-1, where n is number of layers.

V. CONCLUSIONS

5.1. Conclusions

There are several conclusions that can be drawn from this study:

1. The land suitability evaluation system for coastal aquaculture by using ANN and GIS has been developed. The inputs of the system are the thematic map layers in GIS vector format. Afterward, scoring is done by referring to the classification criteria. Rasterizing of vector into raster format is needed to facilitate the map combination processing. The combination map database are classified with ANN, and finally each suitability class is plotted. Training of ANN were referred

(4)

to the result of GIS vector map overlay method with ten land parameters are: water salinity, water DO, water pH, soil texture, soil drainage, distance to water body, risk pollution, land cover, spatial plan and rainfall intensity.

2. The system shows 97 % of accuracy on training process and 96 % on validation process for overall S1, S2, S3, and N classes. This accuracy indicates that the system performance is comparable to the GIS vector overlay. By comparing the map produced by the evaluation system and actual shrimp pond yields proved that the distribution of suitability are located on the same location. Each classes of GIS raster together with ANN and GIS vector are distributed on the same location. Difference of covered area of each classes were not significant, due to the usage of different GIS data format, i.e.: raster and vector.

Since GIS raster with ANN method do the overlay on all map at once, while GIS vector method overlay only two kinds of maps at a time, so that GIS with ANN method can do classification faster.

5.2. Recommendations

The developments of land suitability evaluation system using ANN and GIS for coastal aquaculture at Mahakam Delta, East Kalimantan still has some weakness, such as the accuracy being less than 100 % and there are some area classified as “Unclassified”.

To improve the accuracy of using GIS raster together with ANN, it is recommended: 1. To modify the training dataset with more variations or types of representations or

to add more datasets.

2. To modify the ANN system by using the backpropagation model by modifying the number of hidden layers, iteration number and constants.

(5)

REFERENCES

Bandibas, J. 1995. A Land Evaluation System using Artificial Neural Network Based Expert’s Knowledge and Geographic Information System. GIS Development Journal. Available at: www.gisdevelopment.net/aars/acrs/1998/ps1/ps1023.

shtml via the INTERNET. Accessed 2005, Feb 7.

Bonham-Carter, G.F. 1994. Geographic Information Systems for Geoscientist: Modelling with GIS, Pergamon, Ottawa.

Bappeda Kabupaten Kutai Kertanegara. 2003. Rencana Detail Tataruang Kawasan Delta Mahakam. Bappeda Kutai Kertanegara kerjasama dengan LAPI-ITB, Tenggarong.

Bose, A.N, Ghosh, S.N, Yang, C.T and Mitra,A. 1991. Coastal Aquaculture Engineering, Oxford & IBH Publishing, New Delhi.

De By, R.A. 2000. Principles of Geographic Information Systems, ITC, Enschede, The Netherlands.

De Graaf, G., Jennes, J. 2003. Geographic Information Systems In Fisheries Management and Planning: Technical manual, FAO-Fisheries Technical Paper-449, Rome. Available at: http://www.fao.org/docrep/006/y4816e/

y4816e00. htm via the INTERNET. Accessed at 2005, Jul 12.

Easson, G.L., Barr, D.J.2005. Integration of GIS and ANN for Natural Resource Management. Available at: www.gis.esri.com/library/userconf/Proc96/To150/

PAP126/ P126.htm via the INTERNET. Accessed 2005, Feb 4.

ESRI. 2004. ArcGIS® 9.0 Using ArcMap, ESRI, Redlands-USA.

FAO. 1976. Framework of Land Evaluation, FAO Soils bulletin 32, FAO, Rome. Available at: .http://www.fao.org/documents/show_cdr.asp?url_file=/docrep/

W8522E/ W8522E04.htm via the INTERNET. Accessed 2005, Sept 30.

FAO. 1984. Inland Aquaculture Engineering, UNDP PROGRAMME, FAO, Rome. Available at: http://www.fao.org/documents/show_cdr.asp?url_file=/ docrep/

W8522E/W8522E04.htm via the INTERNET. Accessed 2005, Apr 18.

Fu, Limin. 1994. Neural Network in Computer Intelligence, McGraw-Hill, Inc, New York.

Furey, A., Pitman, D. 2003. Farmnote-Inland Saline Aquaculture, Department of Agriculture, Western Australia. Available at: http://www.agric.wa.gov.au via the INTERNET. Accessed 2005 Feb 28.

Hardjowigeno, S ., Sukardi, Djaenuddin, Suharta, N., Jordens, E.R.. 1996. Land Suitability for Brackishwater Fishpond, Center for Soil and Agroclimate Research, Bogor.

Islam, Q.Z., Rahman, H., Sarkar, M.H., Khan, A.S. 1999. Suitable Site Selection of Shrimp Farming in the Coastal Areas of Bangladesh using Remote Sensing Techniques (4 S Model). Available at: http://www.isprs.org/istanbul2004/

(6)

Ministry of Fisheries and Marine. 2003. Artikel Kepiting, Available at:

http://www.dkp.go.id/ content.php?(=1619) via the INTERNET.

Accessed 2005, Oct 11.

Ministry of Reasearch and Techology. 2005. Teknologi Tepat Guna: Budidaya Udang Windu, Warintek-Menristek. Available at:

http://www.iptek.net/ind/ warintek/

Budidaya_perikanan_idx.php?doc=301 via the INTERNET. Acceseed 2005, Oct 11.

Openshaw, S., Abrahart, R.J. 2000. GeoComputation, Taylor & Francis, London Patterson, D.W. 1996. Artificial Neural Network, Theory and Applications, Prentice

Hall, New York.

Purnomo, A.1992. Pemilihan Lokasi Tambak Udang Berwawasan Lingkungan, Puslitbang Perikanan-Balitbang Pertanian, Jakarta.

Rossiter, David G. 1996. A Theoretical Framework of Land Evaluation, Cornell University. Available at: http://wwwscas.cit.cornell.edu/ landeval/ le_notes/

s494ch1p.htm via the INTERNET . Accessed 2005, Sept 30.

Salam, M.A., Ross, Lindsay G. 1999. Optimizing Sites Selection for Development of Shrimp (Penaeus monodon) and Mud Crab (Scylla serrata) Culture in South Western Bangladesh. Available at: http://www.aquaculture.stir.uk/GISAP/

pdfs/shrimp&crab.pdf via the INTERNET. Accessed 2005 Mar 29.

Soesilo, I. 1995. Pengembangan Prototype Wilayah Pesisir dan Marin, kerjasama BPPT-Bakosurtanal, Jakarta.

Total Indonesia. 2001. Mahakam Delta Land Cover Map Series 1982-1996 , PT. Total Indonesia, Balikpapan, Indonesia.

Total Indonesia. 2004. Upaya Pengelolaan Lingkungan dan Upaya Pemantauan Lingkungan – Survai Seismik 3D Resolusi Tinggi di Lapangan Tambora dan Tunu, Wilayah Kontrak Mahakam Kab. Kutai Kartanegara, Propinsi Kalimantan Timur, PT. Total Indonesia, Balikpapan, Indonesia.

Total Indonesia. 2005a. Tambahan Rencana Pengelolaan Lingkungan dan Rencana Pemantauan Lingkungan – Pengembangan Kegiatan Ekploitasi Minyak dan Gas Bumi di Wilayah Kontrak Mahakam Kab. Kutai Kartanegara, Propinsi Kalimantan Timur, PT. Total Indonesia, Balikpapan, Indonesia.

Total Indonesia. 2005b. Studi Produktivitas Tambak di Kawasan Delta Mahakam Kalimantan Timur: Laporan Akhir, Total Indonesia, Balikpapan, Indonesia. Veelenturf, L.P.J.1995. Analysis and Applications of Artificial Neural Networks,