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优化机器学习和基于包装的混合模型用于山体滑坡易感性测绘:中国成古县的案例研究

Xinxiang Lei^1,2,3,4, Jinbao Liu^1,2, Yichun Du^2,3

¹Shaanxi Agricultural Development Group Co., Ltd, Xi'an, 710000, China.

Scientific reports

|November 27, 2025

概括

这项研究优化了机器学习模型,以映射成都县的山体滑坡易感性. 优化的混合模型包装-物流模型树 (Bag-LMT) 证明了地理危险评估的最佳预测准确性.

科学领域:

地质科学地质科学
环境科学环境科学
机器学习应用机器学习应用

背景情况:

土地滑坡易感性测绘对于降低风险至关重要.
传统方法往往缺乏先进的计算模型的预测能力.
成古县面临着严重的山体滑坡风险,需要准确的预测工具.

研究的目的:

为了比较各种模型的有效性,包括统计和机器学习方法,用于山体滑坡易感性映射.
使用超参数调整优化机器学习模型,以提高预测准确度.
确定成都县滑坡预测最有效的模型.

主要方法:

使用确定因素 (CF) 模型和几种机器学习算法:功能树 (FT),物流模型树 (LMT),交替决策树 (ADT) 和减少错误修剪树 (REPT).
开发了使用包装技术 (包装-FT,包装-ADT,包装-REPT,包装-LMT) 的混合模型.
使用网格搜索进行超参数优化和接收器操作特征 (ROC) 曲线进行准确性评估.

主要成果:

超参数优化的包装-物流模型树 (Bag-LMT) 模型实现了最高的预测准确性.
使用ROC曲线进行的比较分析验证了优化混合动力模型的优异性能.
该研究确定了16个关键的山体滑坡条件因素,影响该地区的山体滑坡发生.

结论:

机器学习模型的优化显著提高了地理危险预测能力.
袋-LMT模型为成都县的山体滑坡易感度绘制提供了强大而准确的工具.
先进的建模技术为有效的土地利用规划和减少灾害风险提供了宝贵的见解.

关键词:

包装式混合动力模型的包装.确定性因素方法的确定性因素方法.成古市成古市的城市.土地滑坡易受影响的地图.机器学习优化优化

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