Abstract
The article considers neural-network methods and technologies, which are relatively new even for many researchers and experts, as applied to water–environmental regulation. The efficiency of neural networks in this line of studies is due to their self-training, and the ensuing ability to reveal complex nonlinear relationships between the characteristics under control by data processing instruments, consisting of interrelated neurons. The methodology of artificial neural networks and the features of their functioning are described. Training and methodological examples are given to illustrate their potential use. A practical problem, considered as an example of ANN application, is the potential for improving the efficiency of identification of large enterprises polluting natural water among many water users in an industrial region. This is made with the use of data on the concentrations of some priority water-polluting metals at the hydrochemical gages in the Iset river near Ekaterinburg City. The neural-network analysis is shown to detect relationships between individual water quality characteristics at nearby gages. This allowed the conclusion that there exist close logistic economic relationships between water users, which help revealing water pollutants by the water footprint produced by plants working in the same branch. It is also shown that the use of ANN opens new ways for determining the contribution of industrial waste discharges to the level of water pollution by substances of dual genesis (natural and technogenic). The reliability of the conclusions is confirmed by the possibility to use the data on a given hydrochemical gage to satisfactorily predict water quality at a gage further downstream.