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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Autores principales:	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
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2025-10-01
Colección:	Remote Sensing
Materias:	seasonal fire probability annual fire prediction Northern Thailand machine learning random forest fire risk modeling
Acceso en línea:	https://www.mdpi.com/2072-4292/17/19/3378
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