Reconfiguring existing pollutant monitoring stations by increasing the value of the gathered information. (October 2017)

• Record Type: Journal Article
• Authors: Gong, Bing; Ordieres-Meré, Joaquín
• Is Part Of: Environmental modelling & software. Volume 96(2017)
• Page Start: 106
• Page End: 122
• Publication Date: 2017-10
• Subjects: Pollution forecasting -- Monitoring networks reconfigure -- Data mining -- Feature selection -- Case study; 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.2017.06.034
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The objective of this study is to provide a framework for relocating or reconfiguring existing pollution monitoring station networks by using feature selection and data mining techniques. This methodology enables a partial redesign based on the maximization of the available information that is gathered by the pollution networks by the optimal data mining technique. It also considers requirements of the decision makers, like potential target places, etc. Since this methodology is based on the quality of forecasting, it can also be useful for auditing and forecasting. A case study is included in this paper. In light of the prediction results, a new way to relocate the existing monitoring station is proposed. Highlights: A new methodology for the redesign of existing pollution networks is presented. The methodology relies on the local modelling capabilities per pollutant. The methodology can suggest new places to which to move the stations. Feature selection to improve the effectiveness of local models is implemented. Pollution forecast by combining inputs from different places was demonstrated.