Forecasting of groundwater level in hard rock region using artificial neural network |
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| Authors: | Pallavi Banerjee R K Prasad V S Singh |
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| Institution: | (1) National Geophysical Research Institute, Hyderabad, Andhra Pradesh, 500007, India |
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| Abstract: | In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has
already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs
of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has
urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the
aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and
potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a
method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction
tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development
of groundwater resources. |
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| Keywords: | Hard rock aquifer Groundwater level Artificial neural network Forecasting |
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