Hybrid neural modeling for groundwater level prediction

The accurate prediction of groundwater level is important for the efficient use and management of groundwater resources, particularly in sub-humid regions where water surplus in monsoon season and water scarcity in non-monsoon season is a common phenomenon. In this paper, an attempt has been made to develop a hybrid neural model (ANN-GA) employing an artificial neural network (ANN) model in conjunction with famous optimization strategy called genetic algorithms (GA) for accurate prediction of groundwater levels in the lower Mahanadi river basin of Orissa State, India. Three types of functionally different algorithm-based ANN models (viz. back-propagation (GDX), Levenberg–Marquardt (LM) and Bayesian regularization (BR)) were used to compare the strength of proposed hybrid model in the efficient prediction of groundwater fluctuations. The ANN-GA hybrid modeling was carried out with lead-time of 1 week and study mainly aimed at November and January months of a year. Overall, simulation results suggest that the Bayesian regularization model is the most efficient of the ANN models tested for the study period. However, a strong correlation between the observed and predicted groundwater levels was observed for all the models. The results reveal that the hybrid GA-based ANN algorithm is able to produce better accuracy and performance in medium and high groundwater level predictions compared to conventional ANN techniques including Bayesian regularization model. Furthermore, the study shows that hybrid neural models can offer significant implications for improving groundwater management and water supply planning in semi-arid areas where aquifer information is not available.

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