Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India

Abstract

This study presents an artificial neural network (ANN) model predicting values of sodium adsorption ratio (SAR), residual sodium carbonate, magnesium adsorption ratio, Kellys ratio and percent sodium (%Na) in the groundwater of Nanded tehsil. The 50 groundwater samples were analyzed for different physicochemical parameters such as pH, EC, TDS, Ca, Mg, Na, K, Cl, CO3, HCO3, SO4 and NO3, for the pre monsoon season 2012. The ANN model is developed through R programming and compared with MS-Excel software. These parameters were used as input variables in the ANN based groundwater quality indices for irrigation suitability prediction. The best back propagation algorithm and neuron numbers were determined for optimization of the model architecture. The resilient backpropagation algorithm with weight back tracking was used for optimization of seven neurons through sensitive analysis. It showed that a network with seven neurons was highly accurate in predicting the irrigation suitability indices. The relative mean squared error, coefficient of determination (R2) and mean absolute relative error between experimental data and model outputs were calculated. It is observed that is a good agreement between actual data and ANN outputs of groundwater for irrigation suitability indices for training and testing datasets. The spatial distribution maps of measured and predicted values of irrigation indices were prepared using ArcGIS software. Hence, the result confirms that the ANN model is an applied tool to predict the groundwater suitability for irrigation purpose in Nanded tehsil.