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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Artificial Intelligence, Geospatial modeling, Disaster monitoring and management, Climate change

Development of Mid-range Forecast Models of Forest Fire Risk Using Machine Learning

DC Field Value Language Park, Sumin ko Son, Bokyung ko Im, Jungho ko Kang, Yoojin ko Kwon, Chungeun ko Kim, Sungyong ko 2022-12-30T04:08:46Z - 2022-12-28 ko 2022-10 ko
dc.identifier.citation Korean Journal of Remote Sensing, v.38, no.5, pp.781 - 791 ko
dc.identifier.issn 1225-6161 ko
dc.identifier.uri -
dc.description.abstract It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used. ko
dc.language 한국어 ko
dc.publisher 대한원격탐사학회 ko
dc.title Development of Mid-range Forecast Models of Forest Fire Risk Using Machine Learning ko
dc.title.alternative 기계학습 기반의 산불위험 중기예보 모델 개발 ko
dc.type ARTICLE ko
dc.type.rims ART ko
dc.identifier.doi 10.7780/kjrs.2022. ko
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