Integrating geospatial intelligence and machine learning for flood susceptibility mapping

Abstract Flood susceptibility mapping using machine learning models and remote sensing datasets has emerged as an effective approach for identifying flood-prone areas. The main objective of this study was to evaluate flood susceptibility using five ML algorithms: Extreme Gradient Boosting (XGBoost),...

תיאור מלא

מידע ביבליוגרפי

Principais autores:	Mehdi Rahimi, Bahram Malekmohammadi, Mohammad Karimi Firozjaei, Reza Kerachian, Jamal Jokar Arsanjani, Mou Leong Tan, Joseph Awange, Dragan Savic, Qingyun Duan, Amir AghaKouchak
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Nature Portfolio 2026-02-01
סדרה:	Scientific Reports
נושאים:	Flood mapping Susceptibility Machine learning Geospatial data analysis Mapping
גישה מקוונת:	https://doi.org/10.1038/s41598-026-41014-3
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!