Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan.
Int J Environ Res Public Health. 2021 Jan 26;18(3):1072. doi: 10.3390/ijerph18031072.
This article presents a geographic information system (GIS)-based artificial neural network (GANN) model for flood susceptibility assessment of Keelung City, Taiwan. Various factors, including elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index, were generated using a digital elevation model and LANDSAT 8 imagery. Historical flood data from 2015 to 2019, including 307 flood events, were adopted for a comparison of flood susceptibility. Using these factors, the GANN model, based on the back-propagation neural network (BPNN), was employed to provide flood susceptibility. The validation results indicate that a satisfactory result, with a correlation coefficient of 0.814, was obtained. A comparison of the GANN model with those from the SOBEK model was conducted. The comparative results demonstrated that the proposed method can provide good accuracy in predicting flood susceptibility. The results of flood susceptibility are categorized into five classes: Very low, low, moderate, high, and very high, with coverage areas of 60.5%, 27.4%, 8.6%, 2.5%, and 1%, respectively. The results demonstrate that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area including the financial center of the city, can be categorized as high to very high flood susceptibility zones.
本文提出了一种基于地理信息系统（GIS）的人工神经网络（GANN）模型，用于评估台湾基隆市的洪水易感性。使用数字高程模型和 LANDSAT 8 图像生成了各种因素，包括海拔、坡度角、坡度方向、汇流累积、流向、地形湿度指数（TWI）、排水密度、降雨量和归一化差异植被指数。采用 2015 年至 2019 年的历史洪水数据（包括 307 次洪水事件）进行洪水易感性比较。使用这些因素，基于反向传播神经网络（BPNN）的 GANN 模型用于提供洪水易感性。验证结果表明，得到了令人满意的结果，相关系数为 0.814。还对 GANN 模型与 SOBEK 模型进行了比较。比较结果表明，所提出的方法可以在预测洪水易感性方面提供良好的准确性。洪水易感性的结果分为五类：极低、低、中、高和极高，覆盖面积分别为 60.5%、27.4%、8.6%、2.5%和 1%。结果表明，研究区近 3.5%的地区，包括城市核心区和人口极其密集的地区，包括城市的金融中心，可归类为高到极高洪水易感性区域。
