Identification of a Contaminant Source Location in a River System Using Random Forest Modelsopen access
- Authors
- Lee, Yoo Jin; Park, Chuljin; Lee, Mi Lim
- Issue Date
- Apr-2018
- Publisher
- MDPI
- Keywords
- contaminant; sensor network; river system; source identification; random forest
- Citation
- WATER, v.10, no.4
- Indexed
- SCIE
SCOPUS
- Journal Title
- WATER
- Volume
- 10
- Number
- 4
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150267
- DOI
- 10.3390/w10040391
- ISSN
- 2073-4441
- 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.
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