DOI: 10.1016/j.watres.2019.02.027
Scopus记录号: 2-s2.0-85062478863
论文题名: Water characterization and early contamination detection in highly varying stochastic background water, based on Machine Learning methodology for processing real-time UV-Spectrophotometry
作者: Asheri Arnon T. ; Ezra S. ; Fishbain B.
刊名: Water Research
ISSN: 431354
出版年: 2019
卷: 155 起始页码: 333
结束页码: 342
语种: 英语
英文关键词: Early contamination detection
; Event detection system
; Spectrophotometry
; Water quality
英文摘要: Water is a resource that affects every aspect of life. Intentional (terrorist or wartime events) or accidental water contamination events could have a tremendous impact on public health, behavior and morale. Quick detection of such events can mitigate their effects and reduce the potential damage. Continuous on-line monitoring is the first line of defense for reducing contamination associated damage. One of the available tools for such detection is the UV-absorbance spectrophotometry, where the absorbance spectra are compared against a set of normal and contaminated water fingerprints. However, as there are many factors at play that affect this comparison, it is an elusive and tedious task. Further, the comparison against a set of known fingerprints is futile when the water in the supply system are a mix, with varying proportions, of water from different sources, which differ significantly in their physicochemical characteristics. This study presents a new scheme for early detection of contamination events through UV absorbance under unknown routine conditions. The detection mechanism is based on a new affinity measure, Fitness, and a procedure similar to Gram based amplification, which result in a flexible mechanism to alert if a contamination is present. The method was shown to be most effective when applied to a set of comprehensive experiments, which examined the absorbance of various contaminants in drinking water in lab and real-life configurations. Four datasets, which contained real readings from either laboratory experiments or monitoring station of an operational water supply system were used. To extend the testbed even further, an artificial dataset, simulating a vast array of proportions between specific water sources is also presented. The results show, that for all datasets, high detection rates, while maintaining low levels of false alarms, were obtained by the algorithm. © 2019 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/121889
Appears in Collections: 气候变化事实与影响
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作者单位: Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Israel; Mekorot, National Water Company of Israel, Israel
Recommended Citation:
Asheri Arnon T.,Ezra S.,Fishbain B.. Water characterization and early contamination detection in highly varying stochastic background water, based on Machine Learning methodology for processing real-time UV-Spectrophotometry[J]. Water Research,2019-01-01,155