通过信息价值合机器学习模型进行滑坡易感性评估
Yamei Wang1, Zizhao Zhang1,2, Xikun Yu1
1School of Geology and Mining Engineering, Xinjiang University, Urumqi, China.
PloS one
|October 21, 2025
概括
新疆的山体滑坡易受影响
科学领域:
- 地质科学 地质科学
- 地质危险评估项目
- 机器学习在地球科学中的应用
背景情况:
- 在新疆天山山脉经常发生山体滑坡和崩塌,造成重大风险.
- 准确的滑坡易感性评估对于地质危险的预防和缓解至关重要.
研究的目的:
- 开发和评估模型,精确地评估天山北坡经济带的山体滑坡易感性.
- 确定最可靠的模型来预测该地区的山体滑坡事件.
主要方法:
- 选择了10个滑坡条件因素用于多对线性分析.
- 开发并比较了信息价值-最大 (I-MaxEnt) 和信息价值-逻辑回归 (I-LR) 结合模型.
- 使用的接收器运行特征 (ROC) 曲线和现场验证以评估准确性.
主要成果:
- 信息价值-逻辑回归 (I-LR) 结合模型实现了0.941.94的曲线下面面积 (AUC) 的优势.
- 与I-MaxEnt模型相比,I-LR模型的准确性和可靠性更高.
- 现场验证证实I-LR模型的结果与实际的山体滑坡情况非常一致.
结论:
- I-LR合模型非常适合在天山北坡经济带进行山体滑坡易感性评估.
- 这项研究为研究区域的防灾和减灾战略提供了可靠的基础.
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