Prediction of water table depth in a hard rock basin by using artificial neural network

Abstract

The objectives of this study were to determine the factors that influence and control the water table fluctuation in a specific geomorphologic situation, to develop a forecasting model and examine its potential in predicting water table depth using limited data. Prediction of region specific water table fluctuation would certainly guide the way towards conceiving, designing and taking scientific measures to ensure sustainable groundwater management. Analysis of change in groundwater table depth, groundwater flow directions within the watershed showed that the influencing factors of rainfall, groundwater draft from near by structures and the resulting fluctuation in groundwater table depth were well correlated in a specific geological situation. Models for prediction of water table depth were developed based on artificial neural networks (ANNs). The study employed multilayer feed forward neural network with backpropagation learning method to develop the model. The neural networks with different numbers of hidden layer neurons were developed using 4 years (2005 - 2008) monthly rainfall, potential evapotranspiration (PET), and water table depth from nearby, influencing wells data as input and one month ahead water table depth as output. The best model was selected based on the root mean square error (RMSE) of prediction using independent test data set. The results of the study clearly showed that ANN can be used to predict water table depth in a hard rock aquifer with reasonably good accuracy even in case of limited data situation. Key words: Water table fluctuation, rainfall, groundwater draft, ANN.