Spatial event classification using simulated water quality data. (March 2016)

• Record Type: Journal Article
• Authors: Oliker, Nurit; Ohar, Ziv; Ostfeld, Avi
• Is Part Of: Environmental modelling & software. Volume 77(2016:Mar.)
• Page Start: 71
• Page End: 80
• Publication Date: 2016-03
• Subjects: Water distribution systems -- Multiple sensors -- Water security -- Event detection -- Contamination simulation -- EPANET-MSX; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.11.013
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study deals with the integration of contamination simulations and a spatial event detection model. The simulation of contaminant intrusion includes detailed chemical-specific reactions within a multi-species water quality model. This set-up generates a scenario of contaminant distribution and produces a continuous multiple sensor stations database. Three organophosphates pesticides, Chlorpyrifos, Malathion, and Parathion, are modeled as possible contaminants. The event detection model comprises both local and spatial data analysis. The local model applies a previously developed single-sensor event detection model with a higher alert threshold that reduces false alarm rates. The spatial model considers upstream sensor datasets which are examined for their uniqueness and mutual resemblance in a sliding time window. The model utilizes outlier detection, data analyses, and network hydraulics for the detection of suspicious spatial trends. The proposed algorithm is capable of detecting events with low contamination signatures and spatial influence. Two case studies are explored and compared to the single sensor model. The proposed methodology resulted in a lower number of false alarms compared to the previous single sensor event detection modeling approach. Graphical abstract: Highlights: Integration of event detection model with simulated contamination events. Spatial event detection and classification. Sensor measurements generated by chemistry simulations of organophosphates.