Prediction of groundwater level fluctuations by artificial neural network models : A case study
In the last few years, due to excessive exploitation of groundwater resources, the water level has gone down in many parts of India. According to a data from the Central Ground Water Board (CGWB), the average groundwater level in India has decreased by 61 per cent between 2007 and 2017, due to which the availability and quality of water are taking serious problems day-by-day. According to the CGWB (2017), 313 blocks out of a total of 6881 blocks are Critical, 1186 blocks are over exploited, and 100 blocks fall into the category of saline. And there are 94 such blocks where the availability of groundwater is significantly less. The overall development and management of groundwater resources are critical because of the water crisis arising from the declining level of groundwater resources and its immense importance in the social and economic growth of the country. Accurate forecasting of groundwater levels is essential for efficient management of groundwater resources and sustainable development of ecosystems. In this study, Multi-Layer Perceptron (MLP) artificial neural network (ANN) was used to predict groundwater levels in a selected aquifer system. The development of the ANN model included data on rainfall, temperature and river water level and groundwater level. The ANN model was trained using the gradient decant algorithm (GDM). The said model was applied and validated at seven different observation locations, and the estimated capacity of the ANN model was developed for each location. This was assessed using four statistical indicators [Bias, RMSE, NSE and MSE] as well as visual testing. Based on the results of this study, the Neural Network Model (ANN) was found to be efficient for forecasting monthly groundwater levels at almost all selected observation locations. The study concluded that artificial neural network techniques (ANNs) could be used efficiently to predict groundwater level fluctuations, especially in data-scarce situations.
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