使用物流回归,随机森林和决定和回归树模型在孟加拉国查托格拉姆区的基于GIS的滑坡易感性绘图
Md Sharafat Chowdhury1,2, Md Naimur Rahman1, Md Sujon Sheikh1
1Department of Geography and Environment, Jahangirnagar University, Savar, Dhaka, Bangladesh.
Heliyon
|January 1, 2024
概括
这项研究绘制了孟加拉国山体滑坡易感性的地图.
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:
- 越来越多的山体滑坡频率和相关损害需要有效的灾害管理策略.
- 现有的山体滑坡评估方法缺乏普遍的"最佳"方法,强调需要进行区域评估.
- 孟加拉国丘陵地区易受山体滑坡的影响,需要进行局部敏感性测绘.
研究的目的:
- 开发和验证查特格拉姆地区的山体滑坡易感性地图 (LSM).
- 为了比较三个机器学习算法的性能:物流回归 (LR),随机森林 (RF) 和决策和回归树 (DRT).
- 识别高滑坡易受影响的地区,以进行有针对性的危险管理.
主要方法:
- 利用16个条件因素 (地形,水气候,地质,人为) 来建模滑坡易感性.
- 使用机器学习算法:物流回归 (LR),随机森林 (RF) 和决策和回归树 (DRT).
- 通过使用山体滑坡库存数据库 (255个地点) 验证模型,80%的培训数据和20%的测试数据.
主要成果:
- 滑坡易感性地图 (LSMs) 显示,查特格拉姆地区的9-12%是高度易感的.
- 高度敏感的区域集中在具有积极侵蚀和剥光的山脉中.
- 接收器操作特征 (ROC) 值显示所有算法都具有很高的模型准确性 (训练:0.917-0.947;测试:0.905-0.963).
结论:
- 后勤回归 (LR) 的预测率最高,而决策和回归树 (DRT) 的成功率最高.
- 对于评估该地区的山体滑坡易感性,DRT和LR模型被认为更为现实.
- 产生的LSM是查特格拉姆地区当地范围的山体滑坡危险管理的宝贵工具.
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