Artificial Neural Network Technique for Groundwater Modelling of Jaspura Block

Abstract

As a result of economic development and climatic change, arid and semi-arid areas are confronted with significant difficulties in the management of limited freshwater resources. For the most part, groundwater is the most significant water supply in these regions. Groundwater level prediction is a critical component of appropriate sustainable development and must be done correctly. The use of physical-based models is often used in the modelling and prediction of groundwater. However, owing to data shortages in many arid and semi-arid regions, they are not relevant in these areas. The usefulness of data-driven techniques in modelling complicated and nonlinear hydrological processes has been shown in many studies. It is the implementation and comparison of four algorithm-based models for forecasting groundwater levels that is the focal point of this research. This article compares the different Artificial Neural Network method and applies to the Jaspura Block of Banda Districts which is part of the Yamuna River Basin. For the forecast of groundwater levels four distinct algorithms Levenberg Marquardt, Gradient Descent, Scaled Conjugate Gradient and Bayesian regularization are used for optimal design. The ANN training data for input is collected from Recharge and Discharge data while groundwater level data for output layer were being used. The best algorithm comes to the Levenberg Marquardt algorithm in comparison with the other algorithms.