基于机器学习的评估,对越南中部地区的山体滑坡易感性区域范围的变化
Raja Das1, Pham Van Tien2, Karl W Wegmann1,3
1Center for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina, United States of America.
PloS one
|October 25, 2024
概括
由于气候变化,越南的台风引起的山体滑坡正在增加,特别是在北部和东部. 每年的降雨量和海拔是关键因素,为关键的风险降低策略提供了信息.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 气候科学 气候科学
背景情况:
- 越南经常发生的山体滑坡是由台风等极端天气事件引发的,威胁着生命和基础设施.
- 气候变化,包括更强烈的风暴,正在加剧全国各地的山体滑坡易感性.
研究的目的:
- 为了分析越南中部多年来时空滑坡易感度的变化.
- 调查因果因素对概率性的山体滑坡事件的影响.
- 为极端气候事件开发和验证强大的山体滑坡易感性模型.
主要方法:
- 利用了台风Ketsana (2009),热带风暴Podul (2013) 和台风Molave (2020) 的多时间滑坡库存.
- 使用随机森林机器学习算法与九个因果因素构建事件后易感模型.
- 量化和空间映射的模型不确定性.
主要成果:
- 观察到越南北部/东部高易感性区域的扩大和中部/南部地区的减少后莫拉维与后Ketsana相比.
- 确定年平均降雨量和地形高度是最重要的滑坡预测因素,具有非线性关系.
- 实现了高模型性能,曲线下面积 (AUC) >95%,精度>89%,灵敏度>90%.
结论:
- 土地滑坡易感性模式的变化表明,与不断变化的水气候条件相关的增加.
- 该研究为在容易发生山体滑坡的地区优先考虑缓解,降低风险和土地利用规划提供了关键的见解.
- 多时间分析对于理解区域易感性趋势和告知适应性策略至关重要.
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