PROGNOZOWANIE ZMIAN JAKOŚCI WÓD PODZIEMNYCH W UKŁADZIE PRZESTRZENNYM Z WYKORZYSTANIEM SIECI NEURONOWYCH (z 37 fig.)

Abstract

SPATIAL PREDICTIONS OF GROUNDWATER QUALITY CHANGES USING NEURAL NETWORKS (with 37 Figs.)

Abstract. This paper presents using neural networks in spatial prediction of groundwater quality changes on the base of existing database. This database consists of results of regional groundwater quality monitoring of the upper Vistula river basin carried out in 1993–1994 (Witczak et al., 1994a, b). Data (the results of field and laboratory determinations of physicochemical indicators of groundwater quality) was verified using quality control parameters and statistical analysis. On the verified database were conducted predictive trials to provide values of physicochemical indicators for the monitoring sites with known coordinates and monitoring site classification (on the base of physicochemical indicators values) to the area of known type of land-use. The results of such a study show that neural networks can be succesfully used for spatial prediction of changes in groundwater quality. The condition for reliability of the prognoses is verification of input data loaded to the model.