Many urban cities in Southeast Asia are vulnerable to climate change. However, these cities are unable to take effective countermeasures to address vulnerabilities and adaptation due to insufficient data for flood analysis. Two important inputs required in flood analysis are high accuracy Digital Elevation Model (DEM), and long term rainfall record. This paper presents an innovative and cost-effective flood hazard assessment using remote sensing technology and Artificial Neural Network (ANN) to overcome such lack of data. Shuttle Radar Topography Mission (SRTM) and multispectral imagery of Sentinel-2 are used to derive a high-accuracy DEM using ANN. The improvement of SRTM’s DEM is significant with a 42.3% of reduction on Root Mean Square Error (RMSE) which allows the flood modelling to proceed with confidence. The Intensity Duration Frequency (IDF) curves that were constructed from precipitation outputs from a Regional Climate Model (RCM) Weather Research and Forecasting (WRF) were used in this study. Design storms, calculated from these IDF curves with different return periods were then applied to numerical flood simulations to identify flood prone areas. The approach is demonstrated in a flood hazard study in Kendal Regency, Indonesia. Flood map scenarios were generated using improved SRTM and design storms of 10-, 50- and 100-year re-turn periods were constructed using the MIKE 21 hydrodynamic model. This novel approach is innovative and cost-effective for flood hazard assessment using remote sensing and ANN to overcome lack of data. The results are useful for policy makers to understand the flood issues and to proceed flood mitigation adaptation/measures in addressing the impacts of climate change.

中文翻译：

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使用遥感和人工神经网络克服洪水灾害评估中的数据稀缺

东南亚的许多城市都容易受到气候变化的影响。但是，由于洪水分析的数据不足，这些城市无法采取有效的对策来解决脆弱性和适应性问题。洪水分析所需的两个重要输入是高精度数字高程模型（DEM）和长期降雨记录。本文提出了一种创新的，具有成本效益的洪水灾害评估，该评估利用遥感技术和人工神经网络（ANN）来克服此类数据不足的问题。穿梭雷达地形任务（SRTM）和Sentinel-2的多光谱图像用于使用ANN导出高精度DEM。SRTM的DEM有了显着提高，均方根误差（RMSE）降低了42.3％，这使洪水模型可以放心地进行。在本研究中使用了强度持续时间频率（IDF）曲线，该曲线是根据区域气候模型（RCM）的天气研究和预报（WRF）的降水量构建而成的。然后将根据这些IDF曲线的不同返回周期计算出的设计风暴用于数值洪水模拟，以识别易发洪水区域。该方法在印度尼西亚肯德尔摄政区的洪水灾害研究中得到了证明。使用改进的SRTM生成洪水地图场景，并使用MIKE 21水动力模型构建10、50和100年恢复期的设计风暴。这种新颖的方法具有创新性，并且具有成本效益，可利用遥感和人工神经网络来克服数据不足的洪水灾害评估。