Abstract:
The depletion of groundwater table in some areas causing concerns for the long term sustainability of groundwater resources in India. For the
effective management of groundwater, it is important to know fluctuations of ground water level (GWL). In hydrology, the artificial neural
network (ANN) models have been satisfactorily applied for prediction of stream flow, precipitation, and water quality modelling but their
application to the groundwater sector has been found to be limited. In this study, an ANN model and Hybrid model (HM) have been developed to
predict the groundwater level fluctuations and their performances were compared. HM is developed by comprising both ANN and regression
modeling technique, the output layer of ANN was replaced by a Non-linear regression model (NLRM). The models were developed by employing
rainfall and the past GWL as input and the present GWL as output. The data for the modelling task have been used from a watershed located in
the Sagar distt., Madhya Pradesh, India. The performances of the developed ANN model and hybrid model has been evaluated using standard
statistical measures viz. average arithmetic relative error (AARE), correlation coefficient (R), Nash- Sutcliff Efficiency (E), and Threshold
Statistics (TS). The result indicates that the models can successfully be used for prediction of GWL and comparative study shows that the
performance of hybrid model is better than ANN model.
