土地沉降易感性测绘:对五种模型有效性的比较评估
Lei Zhang1,2, Alireza Arabameri3, M Santosh4,5
1Yantai Nanshan University, Yantai, 265713, China. wztgsci@sina.com.
概括
土地沉降 (LS) 是一个重要的危险,通常是由过度的地下水提取引起的. 这项研究绘制了伊朗的LS地图,确定了关键因素,并使用机器学习模型高准确地预测易受影响的区域.
科学领域:
- 地质危险 地质危险
- 水文学的水文学
- 环境科学 环境科学
背景情况:
- 土地沉降 (LS) 构成一个主要的地质和水文危险,威胁着安全和保障.
- 强烈的含水层开采是土地沉降的主要触发因素.
- 开发准确的LS易感性地图对于风险管理至关重要.
研究的目的:
- 为了创建一个土地沉降库存地图的Daumeghan平原,伊朗.
- 识别和分析LS的致病因素.
- 使用先进的机器学习模型生成和验证LS易感性地图.
主要方法:
- 现场调查确定了123个LS和123个非LS地点.
- 选择了14个LS的致病因素 (地形,地质,水文,人为).
- 包括BA,GWO,BART,BRT和SVR在内的机器学习算法被用来生成易感性地图.
主要成果:
- 该研究生成了五个具有高预测准确度的LS易感性地图.
- 蝙蝠算法 (BA) 模型实现了最高的ROC曲线下的面积 (AUROC) 96.6%,其次是GWO (95.8%).
- 经过验证的模型在预测LS的空间分布方面表现出很好的准确性.
结论:
- 开发的LS易感性地图是预测沉降的有效工具.
- 这些地图可以指导缓解策略,并为土地利用规划提供信息,以减少LS风险.
- 像BA和GWO这样的优化模型在LS易感性建模中显示出卓越的性能.
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