Smart data driven quality prediction for urban water source management

Abstract

A water supply system that integrates water source management, treatment and distribution is a critical infrastructure in urban areas. Traditional water quality research mostly focused on separate aspects, lacking a comprehensive coverage of all aspects, which undermines the prediction accuracies.

In this paper, we propose a smart data analysis scheme to analyze and predict the water quality, considering all the water quality standard indicators. Instead of data output from water treatment, we collect the raw water data directly from water sources. We design two models to predict the water quality: (1) adaptive learning rate BP neural network (ALBP) and (2) 2-step isolation and random forest (2sIRF). We applied these models in the practical urban water supply systems of Oslo and Bergen in Norway. The results show that ALBP is theoretically simple and easy to implement. 2sIRF considers the risk distribution and shows higher prediction accuracy. In addition, we perform the correlation analysis of all the indicators and the importance analysis over different indicators. The domain experts have confirmed that this work is meaningful for future risk control and decision support in urban water supply systems.