Forecasting water resources variables using artificial neural networks / by Gavin James Bowden.

Abstract

A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia.