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通过邻里特征聚合和可解释的机器学习来加强山体滑坡易感性测绘分析:在山谷类型城市规划中预防灾害的影响
Lina Shen1, Xinyang Zhao1, Huajun Liu1
1College of Urban and Environment, Northwest University, China.
Journal of environmental management
|February 28, 2026
概括
这项研究使用机器学习模型预测了石泉县的山体滑坡易感性. 优化了邻近特征聚合的CatBoost模型表现最好,揭示了灾害风险的梯度分布.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 空间规划 空间规划 空间规划
背景情况:
- 地质灾害对可持续发展构成重大威胁,需要强有力的风险评估和治理.
- 由于自然灾害,山区河谷地区在空间安全管理方面面临着独特的挑战.
- 土地滑坡易感性测绘对于有效的防灾和减灾策略至关重要.
研究的目的:
- 为了预测石泉县的山体滑坡易感性,这是一个代表性的山区河谷地区.
- 评估和比较6个机器学习模型的性能,用于绘制山体滑坡易感性的地图.
- 提出一个优化的预测方法,并制定有针对性的城市防灾战略.
主要方法:
- 评估了六种机器学习模型:物流回归 (LR),支持向量分类 (SVC),BP神经网络,随机森林分类器 (RFC),CatBoost和LightGBM (LGBM).
- 提出并应用了一种名为"邻近特征聚合 (NFA) "的优化技术.
- 在中国的石泉县进行了山体滑坡易感性测绘.
主要成果:
- 在评估的机器学习模型中,CatBoost模型表现出最高的性能.
- 邻近特征聚合 (NFA) 方法提高了山体滑坡易感性预测.
- 石泉县的山体滑坡易感区划显示了沿河谷的梯度分布和线性模式.
结论:
- 机器学习模型,特别是 CatBoost 与 NFA,是用于在山区预测山体滑坡易感性的有效工具.
- 土地滑坡易感性的空间分布模式为区域规划提供了关键的见解.
- 这些发现为在类似的地理区域制定基于社会风险的防灾战略提供了宝贵的参考资料.
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