Ecological application of evolutionary computation: Improving water quality forecasts for the Nakdong River, Korea

Abstract

Water quality is an important global issue, requiring effective management, which needs good predictive tools. While good methods for lake water quality prediction have previously been developed, accurate prediction of river water quality has hitherto been difficult. This project combines process-model and data mining approaches through evolutionary methods, resulting in tools for more effective water management. Although the work is still in its preliminary stages, error rates of the predictive models are already around half those resulting from representative applications of either pure process-based or pure data mining approaches.