使用土壤湿度主动被动分数水数据与LSTM模型估计洪水淹没面积
Rekzi D Febrian1, Wanyub Kim1, Yangwon Lee2
1Department of Global Smart City, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究使用深度学习模型与卫星数据来准确估计洪水地区. 该方法显示了改善灾害准备能力和应对能力的前景.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 准确的洪水监测和预测对于灾难准备和减轻至关重要.
- 卫星观测与深度学习相结合,为检测洪水模式提供了先进的功能.
研究的目的:
- 开发和验证深度学习模型,利用卫星数据估计洪水淹没地区.
- 评估土壤湿度主动被动 (SMAP) 分数水 (FW) 的有效性,作为洪水建模中的参考.
主要方法:
- 采用长期短期记忆 (LSTM) 模型,整合了土壤水分,降雨预报和地形.
- 数据集被重新采样到30米的空间分辨率,使用双立方插值进行洪水建模.
- LSTM模型的洪水淹没估计与Sentinel-1 SAR图像和混矩阵指标进行了验证.
主要成果:
- LSTM模型实现了SMAP FW的高精度估计,曲线下的平均面积 (AUC) 为0.93.
- 洪水淹没区域验证的高性能准确度约为0.9.9.
- 较低植被覆盖,季节性水变化和平坦地形的地区观察到最佳表现.
结论:
- 拟议的框架显示了加强洪水灾害准备和抵御能力的方法承诺.
- 该研究强调了深度学习模型,特别是LSTM在利用卫星数据进行洪水估计方面的有效性.
- 在不同的地形条件下,SMAP FW被证明是准确估计洪水面积的宝贵参考.
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