From local to regional compound flood mapping with deep learning and data fusion techniques
From local to regional compound flood mapping with deep learning and data fusion techniques
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DOI：
10.1016/j.scitotenv.2021.146927
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发表时间：
2021-04
影响因子：
9.8
通讯作者：
D. Muñoz;Paul Muñoz;H. Moftakhari;H. Moradkhani
中科院分区：
文献类型：
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作者：
D. Muñoz;Paul Muñoz;H. Moftakhari;H. Moradkhani
复合洪泛是海洋、水文、气象和人为驱动因素共同作用的结果，通常采用联合收割机结合连续或并发过程模拟洪泛动态的水动力学模型进行研究。近年来，卷积神经网络（CNN）和数据融合（DF）技术已成为洪水后映射的有效替代方案，并支持当前复杂物理和动力学建模的努力。然而，这些技术尚未探索大规模（区域）复合洪水绘图。在这里，我们评估了CNN & DF框架在飓风马修（2016年10月）导致的美国东南大西洋沿岸沿着CF地图生成中的性能。该框架融合了来自陆地卫星分析就绪数据（ARD）、双极化合成孔径雷达数据（SAR）和沿海数字高程模型（DEM）的多光谱图像，以中等（30米）空间分辨率制作洪水图。最高的整体精度（97%）和f1分数的永久水/洪水（99/100%）时，ARD，SAR和DEM数据集是现成的和融合。此外，由此产生的CF地图同意以及（80%）与后报洪水指导地图的沿海应急风险评估，可以有效地匹配洪水后高水位标志的美国地质调查局分布在沿海县。我们最终评估的框架与不同的DF替代品，并强调其实用性的大规模复合洪水映射以及水动力模型的校准。这里提出的具有成本效益的方法，可以有效地估计暴露于复合沿海洪水，特别是在数据稀缺地区是有用的。
Compound flooding (CF), as a result of oceanic, hydrological, meteorological and anthropogenic drivers, is often studied with hydrodynamic models that combine either successive or concurrent processes to simulate inundation dynamics. In recent years, convolutional neural networks (CNNs) and data fusion (DF) techniques have emerged as effective alternatives for post-flood mapping and supported current efforts of complex physical and dynamical modeling. Yet, those techniques have not been explored for large-scale (regional) compound flood mapping. Here, we evaluate the performance of a CNN & DF framework for generating CF maps along the southeast Atlantic coast of the U.S. as a result of Hurricane Matthew (October 2016). The framework fuses multispectral imagery from Landsat analysis ready data (ARD), dual-polarized synthetic aperture radar data (SAR), and coastal digital elevation models (DEMs) to produce flood maps at moderate (30 m) spatial resolution. The highest overall accuracy (97%) and f1-scores of permanent water/floodwater (99/100%) are achieved when ARD, SAR and DEM datasets are readily available and fused. Moreover, the resulting CF maps agree well (80%) with hindcast flood guidance maps of the Coastal Emergency Risk Assessment and can effectively match post-flood high water marks of the U.S. Geological Survey distributed in coastal counties. We ultimately evaluate the framework with different DF alternatives and highlight their usefulness for large-scale compound flood mapping as well as calibration of hydrodynamic models. The cost-effective approach proposed here enables efficient estimation of exposure to compound coastal flooding and is particularly useful in data scarce regions.