The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters

TLDR

High salinity in the Murray River causes about $22 million in annual damage to Adelaide water users, and optimal pumping could cut salinity by ~10 %, requiring multi‑week forecasts. This paper investigates using artificial neural networks to forecast water quality parameters. The authors review ANN techniques and apply them to a case study forecasting 14‑day salinity in the River Murray at Murray Bridge, South Australia. The ANN forecasts achieved 5.3–7.0 % average absolute percentage error over four years, with a 6.5 % error in a real‑time 1991 simulation, indicating promising accuracy.

Abstract

This paper presents the use of artificial neural networks (ANNs) as a viable means of forecasting water quality parameters. A review of ANNs is given, and a case study is presented in which ANN methods are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. It is estimated that high salinity levels in the Murray cause $US 22 million damage per year to water users in Adelaide. Previous studies have shown that the average salinity of the water supplied to Adelaide could be reduced by about 10% if pumping from the Murray were to be scheduled in an optimal manner. This requires forecasts of salinity several weeks in advance. The results obtained were most promising. The average absolute percentage errors of the independent 14‐day forecasts for four different years of data varied from 5.3% to 7.0%. The average absolute percentage error obtained as part of a real‐time forecasting simulation for 1991 was 6.5%.