Identification of a Contaminant Source Location in a River System Using Random Forest Models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Yoo Jin | - |
dc.contributor.author | Park, Chuljin | - |
dc.contributor.author | Lee, Mi Lim | - |
dc.date.available | 2020-07-10T04:29:00Z | - |
dc.date.created | 2020-07-06 | - |
dc.date.issued | 2018-04 | - |
dc.identifier.issn | 2073-4441 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/3876 | - |
dc.description.abstract | We consider the problem of identifying the source location of a contaminant via analyzing changes in concentration levels observed by a sensor network in a river system. To address this problem, we propose a framework including two main steps: (i) pre-processing data; and (ii) training and testing a classification model. Specifically, we first obtain a data set presenting concentration levels of a contaminant from a simulation model, and extract numerical characteristics from the data set. Then, random forest models are generated and assessed to identify the source location of a contaminant. By using the numerical characteristics from the prior step as their inputs, the models provide outputs representing the possibility, i.e., a value between 0 and 1, of a spill event at each candidate location. The performance of the framework is tested on a part of the Altamaha river system in the state of Georgia, United States of America. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | QUALITY MONITORING NETWORK | - |
dc.subject | GROUNDWATER POLLUTION | - |
dc.subject | IDENTIFYING SOURCES | - |
dc.subject | RELEASE HISTORY | - |
dc.subject | OPTIMIZATION | - |
dc.subject | SIMULATION | - |
dc.subject | CURVES | - |
dc.subject | CHINA | - |
dc.title | Identification of a Contaminant Source Location in a River System Using Random Forest Models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Mi Lim | - |
dc.identifier.doi | 10.3390/w10040391 | - |
dc.identifier.scopusid | 2-s2.0-85044573628 | - |
dc.identifier.wosid | 000434954900045 | - |
dc.identifier.bibliographicCitation | WATER, v.10, no.4 | - |
dc.relation.isPartOf | WATER | - |
dc.citation.title | WATER | - |
dc.citation.volume | 10 | - |
dc.citation.number | 4 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordPlus | QUALITY MONITORING NETWORK | - |
dc.subject.keywordPlus | GROUNDWATER POLLUTION | - |
dc.subject.keywordPlus | IDENTIFYING SOURCES | - |
dc.subject.keywordPlus | RELEASE HISTORY | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordPlus | CURVES | - |
dc.subject.keywordPlus | CHINA | - |
dc.subject.keywordAuthor | contaminant | - |
dc.subject.keywordAuthor | sensor network | - |
dc.subject.keywordAuthor | river system | - |
dc.subject.keywordAuthor | source identification | - |
dc.subject.keywordAuthor | random forest | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.