A Bio-Inspired Prediction Method for Water Quality in a Cyber-Infrastructure Architecture

The water quality is critical as it sustains life. In order to avoid catastrophic situations a decision support system in the water pollution scenario must offer reliable and on time information. Prediction plays in this case a very important role. The paper presents a biologically inspired method to predict values of a temporal series and how this can be applied to the specific case of water quality monitoring. Historically, the prediction methods evolved from statistical to biologically inspired. The proposed method is based on neural networks and represents the core part of the prediction module of the decision support system we designed. The experimental data was gathered on a major river in Romania and in this paper we exemplify with values for pH and Turbidity.