Modelling of Irrigation Water Quality of Coastal Area Using Back Propagation-Multi Layer Perceptron Artificial Neural Network
Journal Title: Universal Journal of Environmental Research and Technology - Year 2013, Vol 3, Issue 2
Abstract
Irrigation water quality is one of the main yield factor in the cultivation of agricultural and horticultural crops in arid, semi-arid and coastal areas. In past two decades, irrigation water quality and quantity problems increasing severely because of improper management and industrialization. The main aim of this study is to describe the applicability of artificial neural network that can effectively predict quality of irrigation water in the above areas. The study was conducted over 17 villages of coastal Chidambaram Taluk and about 170 samples were collected. Irrigation samples were analysed for Physico-chemical properties, various cationic and anionic constituents outlined. Based on the analysis, 70% of the samples were determined by saline and 30 percent samples were alkaline in reaction. Data obtained from chemical analysis were used in the ANN model to predict pH and electrical conductivity. The results of this study proved that MLPBP-ANN is effectively predicting irrigation water quality of coastal area.
Authors and Affiliations
S. Sathiyamurthi, S. Saravanan
Keywords
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- EP ID EP31816
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