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使用三种机器学习技术进行洪水易感性评估,并比较它们的性能

Tade Mule Asrade1, Sintayehu Adefires Abebe2, Kassahun Birhanu Tadesse3

  • 1Department of Hydraulic and Water Resources Engineering , Debre Markos University , Debre Markos, Ethiopia. tadie_muluie@dmu.edu.et.

Scientific reports
|February 10, 2026
PubMed
概括

机器学习模型准确地预测洪水易感性. 梯度提升和极端梯度提升实现了0.97的准确性,在有效的洪水风险管理方面表现优于随机森林.

关键词:
克河流域的流域易受洪水影响的易感性机器学习算法 机器学习算法遥感数据数据 遥感数据

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