Developing A Model-Based Drinking Water Decision Support System Featuring Remote Sensing And Fast Learning Techniques

Keywords

Data fusion; decision support systems (DSSs); drinking water; forecasting models; machine learning; remote sensing

Abstract

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.

Publication Date

6-1-2018

Publication Title

IEEE Systems Journal

Volume

12

Issue

2

Number of Pages

1358-1368

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/JSYST.2016.2538082

Socpus ID

84964557325 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84964557325

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