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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Acknowledgments
The field study has been financed by INCID, Ministry of Water Resources, Government of India. The authors are grateful to Dr. V. P. Dimri, Director, National Geophysical Research Institute, Hyderabad, India for his kind permission to publish this paper.
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Banerjee, P., Prasad, R.K. & Singh, V.S. Forecasting of groundwater level in hard rock region using artificial neural network. Environ Geol 58, 1239–1246 (2009). https://doi.org/10.1007/s00254-008-1619-z
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DOI: https://doi.org/10.1007/s00254-008-1619-z