Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network

Abstract

This paper addresses the problem of how to diagnose the effect of stormwater infiltration on groundwater quality variables and to capture the complex nonlinear relationships that exist between groundwater quality variables. It is argued that because of the complex nonlinear relationships between the groundwater quality variables, classical linear statistical methods are unreliable and difficult to visualise the results. The application of intelligent techniques, which can analyse the multi-dimensional groundwater quality data with the sophisticated visualisation technique, is vital for sustainable groundwater management.

In this paper, the Kohonen self-organising feature maps (KSOFM) neural network is applied to analyse the effect of stormwater infiltration on the groundwater quality, and diagnose the inter-relationship of the groundwater quality variables in a fractured rock aquifer. Based on the pattern analysis visualised in component planes and U-matrix, the inter-relationships among the groundwater quality variables due to the stormwater infiltration are extracted and interpreted. The pattern distribution of groundwater quality variables due to different aquifer conditions is also analysed.

It is concluded that the KSOFM technique described in this paper provides an effective analysing and diagnosing tool to understand the dynamic in the groundwater quality and to extract knowledge contained in the multi-dimensional data. Finally it has considerable potential not only in groundwater quality monitoring and diagnosis, but also in other environmental areas.