通过机器学习和谷歌地球引擎的整合,加强实时洪水影响评估:一种全面的方法
Nafis Sadik Khan1, Sujit Kumar Roy2, Swapan Talukdar3
1Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
概括
这项研究整合了机器学习和谷歌地球引擎,用于孟加拉国的实时洪水映射. 该方法准确地识别了洪水影响和脆弱地区,有助于灾害管理.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 孟加拉国西北部等易受洪水袭击的地区的洪水造成了大量的生命和财产损失.
- 有效的洪水管理需要及时准确地进行影响评估.
研究的目的:
- 开发一种使用机器学习和谷歌地球引擎进行实时洪水评估的综合方法.
- 为了产生近乎实时的洪水地图和土地使用/土地覆盖面 (LULC) 地图,以进行有效的洪水管理.
主要方法:
- 使用 Sentinel-1 合成孔径雷达 (SAR) 数据和谷歌地球引擎进行洪水映射.
- 采用自动值技术来量化被洪水淹没的地区.
- 利用Sentinel-2数据和机器学习模型 (ANN,RF,SVM) 来进行LULC分析,人工神经网络 (ANN) 显示了最高的准确性 (0.94).
主要成果:
- 为库里格拉姆和拉尔莫尼拉特的2020年洪水生成了近实时的洪水地图.
- 识别了60多万人在洪水高峰期间暴露 (约. 17%的人口).
- 7月份,超过一半的耕地被洪水淹没,这凸显了农业的脆弱性.
结论:
- 集成的SAR,机器学习和云计算方法使当局能够实时监控和准确的LULC映射.
- 这种方法有助于识别脆弱地区,并制定数据驱动的洪水管理策略,以减轻影响.
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