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沿海洪水风险评估使用组合多标准决策与机器学习方法
Mashael M Asiri1, Ghadah Aldehim2, Nuha Alruwais3
1Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia.
Environmental research
|December 31, 2023
概括
沿海洪水风险使用先进的分析层次过程 (AHP) 和机器学习模型进行评估. AHP-DT模型显示出高精度,有助于识别高风险区域,以制定有效的洪水管理策略.
科学领域:
- 环境科学 环境科学
- 适应气候变化 适应气候变化
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 由于气候变化引起的海平面上升,沿海地区面临着不断升级的洪水风险.
- 洪水频率和强度的增加与人为活动和计划外发展有关.
- 有效地绘制洪水易感性地图对于沿海洪水风险管理至关重要.
研究的目的:
- 为了识别和绘制伊朗班达尔阿巴斯的沿海洪水易感性.
- 评估整体机器学习模型对沿海洪水预测的性能.
- 在不同的气候变化场景下评估洪水风险 (RCP 2.6和RCP 8.5).
主要方法:
- 分析层次流程 (AHP) 的应用,用于多标准决策.
- 组合建模结合AHP与支持矢量机器 (AHP-SVM) 和决策树 (AHP-DT).
- 使用弗里德曼和威尔科克森签名的等级测试,准确性,敏感性和特异性指标进行统计验证.
主要成果:
- AHP-DT模型实现了0.95.95的高曲线下面积 (AUC).
- 洪水易感性测绘显示,由于地形,雷达克河流域北部和西部地区的风险低至非常低.
- 在东部,中部地区确定了高洪水易感性 (CFSM),沿海人口面临低至中等暴露.
结论:
- 集成机器学习模型,特别是AHP-DT,对于沿海洪水易感性测绘是有效的.
- 地形特征显著影响沿海地区的洪水风险.
- 这些发现为在脆弱的沿海地区实施有针对性的降低风险战略提供了关键数据.
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