一个自动编码器驱动的深度学习地理空间方法来分析大莫达河上游和中游盆地的洪水脆弱性
Rohit Srinivas Thappitla1, Vasanta Govind Kumar Villuri2, Satish Kumar3
1Geomatics Division, Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India.
Scientific reports
|September 30, 2025
概括
本研究引入了一种新的深度学习方法,用于在数据稀缺的地区绘制洪水脆弱性的地图. 一个由卷积神经网络 (CNN) 领导的自动编码器有效地识别洪水风险区域,帮助积极的减缓策略.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 机器学习应用 机器学习应用
背景情况:
- 绘制洪水脆弱性的地图对于灾害管理至关重要.
- 由于洪水库存数据有限,传统的监督机器学习 (ML) 方法在数据稀缺的地区扎.
- 在ML的进步为改善洪水预测确定性提供了潜力.
研究的目的:
- 开发和评估一种新的深度学习方法,用于在数据有限的环境中评估洪水脆弱性.
- 创建一个大莫达河流域的洪水风险区分地图,使用地理空间数据和先进的ML.
- 确定影响洪水脆弱性的关键因果因素.
主要方法:
- 一个卷积神经网络 (CNN) 带领的自动编码器被用于特征提取和维度减少.
- 基于经过处理的地理空间数据,使用K-means集群来划分洪水风险区.
- 用11个因果因素 (地理空间层) 来描述研究区域.
- 模型性能使用重建指标 (MSE,精度,回忆,准确性) 和基于集群的指数进行评估.
主要成果:
- 洪水风险区分地图显示,研究区域的92%是安全的.
- 不到8%的地区面临中度至非常高的洪水风险.
- 排水密度被确定为影响洪水脆弱性预测的重要因素.
- 发现某些因素,如Aspect,将噪音引入模型.
结论:
- 拟议的CNN主导的自动编码方法对洪水脆弱性绘制有效,特别是在数据稀缺的地区.
- 该方法提供了可靠的洪水风险区分,支持对减轻风险的知情决策.
- 了解诸如排水密度等因果因素的影响对于准确的洪水风险评估至关重要.
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