Groundwater level prediction in Visakhapatnam district, Andhra Pradesh, India using Bayesian Neural Networks

Abstract

Abstract Groundwater level and rainfall measurements from 37 borewells in the Visakhapatnam district, Andhra Pradesh, India from 2002 to 2021 were analysed using Bayesian Neural Networks (BNN) to comprehend the predictability. We found chaotic dynamics in the groundwater and rainfall data, but a dominant trend component was seen in the groundwater from phase plots. Dynamics suggest the presence of self-organized criticality/chaos in the groundwater changes over decadal time scales. We used BNN prediction models (i) Non-linear Autoregressive (NAR) (ii) Non-linear Input Output and (NIO) (iii) Non-linear Autoregressive Exogenic Input (NARX) to predict the groundwater level changes with rainfall as an exogenic input. We noticed ~ 94 to 95% prediction accuracy with the NAR model with optimal inputs and ~ 1% improvement with added exogenic input. Interestingly, the study indicates that the (i) dynamics of the groundwater differ significantly from rainfall and temperature in the region (ii) the Non-Linear Autoregressive Model considered based on the self-organized dynamics of groundwater level changes is robust in providing prediction accuracy up to ~ 95% (iii) dynamics of rest of the 5% may be due to the presence of extreme events, whose dynamics are closely related to random processes of the changes attributed to randomly varying manmade and weather changes.