Aqua Site Classification Using Neural Network Models

N. DeepaKaliyaperumal Ganesan

DOI 10.7160/aol.2016.080405
No 4/2016, December
pp. 51-58

Deepa, N. and Ganesan, K. (2016) “Aqua Site Classification Using Neural Network Models", AGRIS on-line Papers in Economics and Informatics, Vol. 8, No. 4, pp. 51 - 58. ISSN 1804-1930. DOI 10.7160/aol.2016.080405.

Abstract

India being one of the major producers of fish contributes 5.5 percent of global fish production and ranks second in the world after China. The production of aquaculture mainly depends on the quality of land selected for aqua farming. Neural Network algorithms have been applied to classify the aquaculture sites based on 6 input variables viz., water, soil, support, infrastructure, input and risk factor. An artificial neural network (ANN) consists of huge number of interconnected elements called neurons that work together to solve a specific problem. An Artificial Neural network can be used for classification, prediction, pattern recognition etc., through a learning process. In this paper, the models were constructed using three Neural Network algorithms viz., Back Propagation Network (BPN), Radial Basis Function (RBF) and Linear Vector Quantization (LVQ). The models classify each aquaculture site into 3 classes viz., suitable, moderate and unsuitable. From the results of the three models, it has been found that Radial Basis Function model not only gives accurate results but also time taken for training the dataset is less when compared with the other two Neural Network models. The results obtained from the neural network models were validated with the results of the fuzzy model.

Keywords

Neural Networks, Aquaculture, Land Classification, Back Propagation Network, Radial Basis Function, Linear Vector Quantization.

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