MLEWS: a series of software systems to implement machine learning for geo-hazards risk early warning based on meteorological factors

Sudden and frequent geo-hazards pose serious threats to life and property, highlighting the need for an integrated, multi-disciplinary early warning system (EWS). However, incorporating big data and machine learning into traditional EWSs brings challenges such as complex model structures, inefficien...

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Bibliografische Detailangaben

Hauptverfasser:	Yanhui Liu, Yurou Li, Xiaoli Wang, Ruihua Xiao, Pinggen Zhou
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Taylor & Francis Group 2026-12-01
Schriftenreihe:	Geomatics, Natural Hazards & Risk
Schlagworte:	Geo-hazards early warning model early warning system machine learning random forest
Online-Zugang:	https://www.tandfonline.com/doi/10.1080/19475705.2026.2616640
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