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

Abstract

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 (S 1 ), 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…dst