Artificial intelligence technologies in surface water quality monitoring

Abstract

Abstract: Water quality monitoring networks are designed to detect, evaluate, arid quantify past, present, and emerging water quality problems and trends. A review of strategies to design monitoring networks suggests that a structured and consistent design methodology is for the most part missing. Monitoring network design is a complex task that requires an optimal configuration to ensure the maximum information extraction from the water quality data collected. In order to attain an optimal, and ultimately cost‐effective, network design, complementary design techniques or tools are needed. An overview of potentially applicable artificial intelligence technologies, as well as a literature review of promising research undertaken in the water resources area with respect to artificial intelligence techniques are presented. The artificial intelligence technologies examined include expert systems, artificial neural networks, genetic algorithms, and fuzzy logic systems.