Developing a Model-Based Drinking Water Decision Support System Featuring Remote Sensing and Fast Learning Techniques

Timely adjustment of operating strategies in drinking water treatment in response to water quality variations in both natural and anthropogenic causes is a grand technical challenge. One essential approach is to develop and apply integrated sensing, monitoring, and modeling technologies to provide early warning messages to plant operators. This paper presents a thorough literature review of the technical methods, followed by the development of a model-based decision support system (DSS). The DSS aims to aid water treatment plant operators by analyzing source water impacts. This model-based DSS features remote sensing and fast learning techniques that can be easily applied by end-users and provide a visual depiction of spatiotemporal variations in source water quality parameters of interest. The system is able to forecast the trend of water quality one day into the future at a specific location and nowcast water quality at water intake locations, thus helping the assessment of water quality in finished water against treatment objectives. The model-based DSS was assessed in a case study at a water treatment plant in Las Vegas, United States.