A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts

Abstract

A rapid assessment method for evaluating the impacts of groundwater abstraction on river flow depletion has been developed and tested. A hybrid approach was taken, in which a neural network model was used to mimic the results from numerical simulations of interactions between groundwater and rivers using the SHETRAN integrated catchment modelling system. The use of a numerical model ensures self-consistent relationships between input and output data which have a physical basis and are smooth and free of noise. The model simulations required large number of input parameters and several types of time series and spatial output data representing river flow depletions and groundwater drawdown. An orthogonal array technique was used to select parameter values from the multi-dimensional parameter space, providing an efficient design for the neural network training as the datasets are reasonably independent. The efficiency of the neural network model was also improved by a data reduction approach involving fitting curves to the outputs from the numerical model without significant loss of information. It was found that the use of these techniques were essential to develop a feasible method of providing rapid access to the results of detailed process-based simulations using neural networks.