机器学习模型优化用于Kosi大风扇,喜马拉雅前陆盆地,印度的洪水易感区划
Aman Arora1, Purna Durga G2, Manish Pandey3,4
1Université Gustave Eiffel, GERS-LEE, 44344, Bouguenais, France.
Scientific reports
|September 24, 2025
概括
这项研究优化了机器学习模型,用于Kosi大风扇的洪水易感性映射. 人工神经网络多层感知器 (ANN-MLP) 模型显示出卓越的性能,增强了洪水风险管理策略.
科学领域:
- 地质科学和遥感技术
- 环境科学 环境科学
- 环境建模中的人工智能
背景情况:
- 科西大风扇面临严重的洪水风险,需要准确的易感性测绘以进行有效的管理.
- 喜马拉雅山林地盆地的动态环境条件需要先进的建模技术.
研究的目的:
- 评估和优化五个机器学习算法用于Kosi大风扇的洪水易感区分.
- 确定影响该地区洪水易感性的关键条件因素.
- 创建可靠的洪水易感性地图,以改善风险评估和降低风险.
主要方法:
- 利用来自各种遥感和辅助数据源的19个条件因素的数据集.
- 训练并验证了五种机器学习算法:随机子空间,J48,最大 (MaxEnt),人工神经网络多层感知器 (ANN-MLP) 和基于生物地理的优化.
- 使用包括精度,真实技能统计 (TSS),卡帕和曲线下的面积 (AUC) 在内的指标评估模型性能.
主要成果:
- 该ANN-MLP模型实现了最高的性能,精度=0.982,TSS=0.964和卡帕=0.964.
- 最大 (MaxEnt) 也在环境建模中表现出强的表现.
- 标准化差异植被指数 (NDVI),高度,距离道路,降雨量和距离河流的距离被确定为最重要的洪水易感性因素.
结论:
- 先进的机器学习,特别是ANN-MLP,显著提高了洪水易感性评估的准确性和可靠性.
- 该研究为科西大风扇提供了有价值的洪水易感性地图,有助于风险识别和减轻规划.
- 这项研究支持在复杂环境中更有效地管理洪水风险和应对灾害的准备.
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