This study performed flood risk assessment in urbanized areas using geospatial and remotely sensed data for 1990–2005–2020 periods using the linear unmixing model (LUM), random forests (RF), and support vector machines (SVM). The urban mapping results verified with reference data indicated close agreement, with the overall accuracies and Kappa coefficients higher than 88.9% and 0.78, respectively. A remarkable increase of 14.9% in urban area was observed for the period 2005–2020, compared to 7.2% during the period 1990–2005. The results of flood risk modeling revealed that RF produced slightly more accurate results than SVM. The flood risk areas aggregated with urban maps showed that the larger urban flood risk area was especially observed for the period 2005 − 2020. The urban areas of high/very high and medium flood risks calculated for 2020 were 19.7% and 20%, respectively. Such flood risk areas were also overlaid with the population density for urban planning and management.

本研究使用 1990 年至 2005 年至 2020 年期间的地理空间和遥感数据，使用线性分解模型 (LUM)、随机森林 (RF) 和支持向量机 (SVM)，对城市化地区进行了洪水风险评估。与参考数据验证的城市测绘结果一致，总体准确度和Kappa系数分别高于88.9%和0.78。2005 年至 2020 年期间，城市面积显着增加了 14.9%，而 1990 年至 2005 年期间为 7.2%。洪水风险建模的结果表明，RF 产生的结果比 SVM 稍微准确一些。与城市地图相结合的洪水风险区域表明，在 2005 年至 2020 年期间，尤其观察到较大的城市洪水风险区域。2020 年计算的高/非常高和中等洪水风险的城市地区分别为 19.7% 和 20%。这些洪水风险区域还与城市规划和管理的人口密度重叠。