Artificial neural network model as a potential alternative for groundwater salinity forecasting

SummaryThe paper evaluates the prospect of artificial neural network (ANN) simulation over mathematical modeling in estimating safe pumping rate to maintain groundwater salinity in island aquifers. Feed-forward ANN model with quick propagation (QP) as training algorithm has been used to forecast the salinity under varied pumping rates. The accuracy, generalization ability and reliability of the model are verified by real-time field data. The model is trained with 2 years of real-time field data and prediction on water quality with varying pumping rate is made for a span of 5 years. The same is then compared with both real-time field data and the prediction based on SUTRA (Saturated–Unsaturated Transport) computations. The proposed ANN model has surfaced as a simpler and more accurate alternative to the numerical method techniques. The ANN methodology using minimal lag and number of hidden nodes, along with the optimal number of spatial and temporal variables consistently produced the best performing network based simulation models. The prediction accuracy of the ANN model has been extended for another 5 years to further define the limit of pumping at a permissible level of groundwater salinity.

Research highlights
► Application of Artificial Neural Network (ANN) to hydrological quality forecasting.
► ANN as a potential alternative to finite element numerical modeling.
► Scope for ANN modeling for its nonlinear nature in deciphering hydrological data which relates to high level of diversity amongst the parameters.