Gamma test-based MLP based ANN model for groundwater fluctuation forecasting in Kanpur District, Uttar Pradesh

SHASHINDRA KUMAR SACHAN; ARPAN SHERRING; DERRICK M DENIS

doi:10.21921/jas.v10i02.12720

SHASHINDRA KUMAR SACHAN Associate Professor, Deptt. of Agricultural Engineering, Chandrashekhar Azad University of Agriculture and Technology, Kanpur, Uttar Pradesh, India
ARPAN SHERRING Professor, Deptt. of Irrigation and Drainage Engineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh, India
DERRICK M DENIS Professor, Deptt. of Irrigation and Drainage Engineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh, India

DOI: https://doi.org/10.21921/jas.v10i02.12720

Keywords: Neural networks; Forecasting; Gamma test; Groundwater level fluctuations.

Abstract

This study seeks to determine the accuracy of the groundwater level fluctuations forecasted at the Kanpur district of India using artificial neural networks (ANNs). An overview of how gamma tests can be useful together to decrease the huge amount of work involved in the process of trial-and-error in nonlinear modeling method is presented in this study. The results indicated that performance of multilayer perceptron (MLP) based neural network (M-16, architecture 4-18-1) is satisfactory in the groundwater level fluctuations forecasting. The performance assessment shows that the MLP model performs significantly better. In future studies, it might be useful to apply these approaches as a laborious approach for ensuring that the appropriate results are obtained very quickly even though they are time-consuming.