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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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dc.citation.number 22 -
dc.citation.startPage 8213 -
dc.citation.title APPLIED SCIENCES-BASEL -
dc.citation.volume 10 -
dc.contributor.author Kang, Yoojin -
dc.contributor.author Jang, Eunna -
dc.contributor.author Im, Jungho -
dc.contributor.author Kwon, Chungeun -
dc.contributor.author Kim, Sungyong -
dc.date.accessioned 2023-12-21T16:41:48Z -
dc.date.available 2023-12-21T16:41:48Z -
dc.date.created 2021-01-05 -
dc.date.issued 2020-11 -
dc.description.abstract Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea's current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined. -
dc.identifier.bibliographicCitation APPLIED SCIENCES-BASEL, v.10, no.22, pp.8213 -
dc.identifier.doi 10.3390/app10228213 -
dc.identifier.issn 2076-3417 -
dc.identifier.scopusid 2-s2.0-85096379215 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/49287 -
dc.identifier.url https://www.mdpi.com/2076-3417/10/22/8213 -
dc.identifier.wosid 000594106700001 -
dc.language 영어 -
dc.publisher MDPI -
dc.title Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied -
dc.relation.journalResearchArea Chemistry; Engineering; Materials Science; Physics -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor machine learning -
dc.subject.keywordAuthor wildfires -
dc.subject.keywordAuthor susceptibility -
dc.subject.keywordPlus NEURAL-NETWORK -
dc.subject.keywordPlus SUSCEPTIBILITY -
dc.subject.keywordPlus SYSTEM -
dc.subject.keywordPlus MODELS -
dc.subject.keywordPlus INDICATORS -
dc.subject.keywordPlus PREDICTION -
dc.subject.keywordPlus PROVINCE -

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