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Modeling Seasonal Fire Probability in Thailand: A Machine Learning Approach Using Multiyear Remote Sensing Data

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we...

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Автори: Enikoe Bihari, Karen Dyson, Kayla Johnston, Daniel Marc G. dela Torre, Akkarapon Chaiyana, Karis Tenneson, Wasana Sittirin, Ate Poortinga, Veerachai Tanpipat, Kobsak Wanthongchai, Thannarot Kunlamai, Elijah Dalton, Chanarun Saisaward, Marina Tornorsam, David Ganz, David Saah
Формат: Artigo
Мова:Inglês
Опубліковано: MDPI AG 2025-10-01
Серія:Remote Sensing
Предмети:
seasonal fire probability
annual fire prediction
Northern Thailand
machine learning
random forest
fire risk modeling
Онлайн доступ:https://www.mdpi.com/2072-4292/17/19/3378
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