Application of Artificial Ne ural Network Model to Study Arsenic Contamination in Groundwater of Malda District, Eastern India

Abstract

The extent of arsenic contamination in the groundwater has been estimated using artificial neural network (ANN) based on multi-layer perceptron (MLP) architecture. The input data to the ANN comprised samples collected from different arsenic affected blocks of Malda district, West Bengal, India. Each data sample consisted of the amount of arsenic contaminant observed in the groundwater together with other geochemical parameters believed to have some relationship with the corresponding arsenic contaminant. Here, the inputs to the ANN were observed values for pH (acidic-alkalinity ratio), specific conductivity, total dissolved solids (TDS), salinity, dissolved oxygen (DO), redox potential (Eh), and depth of tube well water, while the expected output for training the ANN was the amount of corresponding arsenic contaminant observed in the groundwater. Using the back propagation technique, the ANN model was trained with a subset of the input data. The trained ANN model was then used to estimate the arsenic contamination in groundwater beyond the specified training data. Th equality of the ANN simulations was evaluated in terms of three different error measures; namely, the root mean square error, the mean absolute error, and the percent mean relative error for proper interpretation of the results. We have also used two other me thods for prediction; namely, multiple linear regression and active set support vector regression. Amongst the three methods, the ANN model exhibited better prediction results for predicting the arsenic contamination in groundwater. Based on this methodology, it is possi ble to show that a four-layer feed-forward back propagation ANN model could be used as an acceptable prediction model for estimating the arsenic contamination in groundwater.

Keywords: arsenic contamination, back propagation neural network, groundwater arsenic estimation, active set support vector regression, multiple linear regression