Application of ANN in sketching spatial nonlinearity of unconfined aquifer in agricultural basin

This paper endeavors the growing challenges of groundwater economy in agriculture with information and analysis of the spatial nonlinearity in groundwater depletion due to anthropogenic abstraction and proposes a way to find the water table imprints by judicious application of artificial neural networks (ANN). The results exhibit that groundwater problems and their agricultural consequences are heterogeneous across space and time. While the problems are contemplative and impressionistic, the severity scales varying dimensions. It is found that ANN models are realistic and viable due to their inherent stochastic nature of neural computation using artificial intelligence decoding ingrained nonlinearity and strong synchronicity. The result demonstrates that ANN is capable of recognizing local optimal in a time series analyses and can successfully forecast seasonal variability. It can be used to closely monitor the water variables to meet and anticipate the growing challenges of groundwater resource sustainability and precision irrigation. The model can be leveraged in devising water economy policy and seasonal cropping practices which in turn can aid policies to be tailored to local hydrogeological settings and agro economic realities. While market forces and economic incentive policy can change water use, public initiatives for agricultural groundwater regulation to balance short term economic efficiency with long resource sustainability are urgently needed.