A Fuzzy Markov approach for assessing groundwater pollution potential for landfill siting

dc.citation.epage197en_US
dc.citation.issue2en_US
dc.citation.spage187en_US
dc.citation.volume20en_US
dc.citation.woscount4
dc.contributor.authorChen, WYen_US
dc.contributor.authorKao, JJen_US
dc.contributor.department環境工程研究所zh_TW
dc.contributor.departmentInstitute of Environmental Engineeringen_US
dc.date.accessioned2014-12-08T15:42:35Z
dc.date.available2014-12-08T15:42:35Z
dc.date.issued2002-04-01en_US
dc.description.abstractThis Study presents a Fuzzy Markov groundwater Pollution potential assessment approach to facilitate landfill siting analysis. Landfill siting is constrained by various regulations and is complicated by the uncertainty of groundwater related factors. The conventional static rating method cannot properly depict the potential impact of Pollution on a groundwater table because the groundwater table level fluctuates. A Markov chain model is a dynamic model that can be viewed as a hybrid of probability and matrix models. The probability matrix of the Markov chain model is determined based on the groundwater table elevation time series. The probability reflects the likelihood of the groundwater table changing between levels. A fuzzy set method is applied to estimate the degree of pollution potential, and a case study demonstrates the applicability of the proposed approach. The short- and long-term pollution potential information provided by the proposed approach is expected to enhance landfill siting decisions.en_US
dc.identifier.issn0734-242Xen_US
dc.identifier.journalWASTE MANAGEMENT & RESEARCHen_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/28903
dc.identifier.wosnumberWOS:000175682000010
dc.language.isoen_USen_US
dc.subjectlandfill sitingen_US
dc.subjectgroundwater tableen_US
dc.subjectfuzzy seten_US
dc.subjectMarkov chainen_US
dc.subjectdynamic systemen_US
dc.subjectwmr 485-5en_US
dc.titleA Fuzzy Markov approach for assessing groundwater pollution potential for landfill sitingen_US
dc.typeArticleen_US

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