Flood havoc during 2019 in the Sangu River basin caused widespread damage to residents, crops, roads, and communications in parts of hills in Bangladesh. Developing flood hazard maps can play an essential step in risks management. For this purpose, this study assessed 12 hydro-geomorphological factors, namely, topographic wetness index, elevation, slope, extreme rainfall, land-use and land-cover, soil type, lithology, curvature, drainage density, aspect, height above the nearest drainage, and distance from streams. Maps prepared by individual application of the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP) exhibit validation scores ranging from 0.77 to 0.79. It is found that the ANP-based model under 1-day maximum rainfall denotes a reliable hazard map presenting comparable accuracy to the field results. The hazard map under 100-year return periods shows that a total of 0.71 million population living downstream is prone to “very high” flood because of its lowland morphology, mild slope, and high drainage density. Alarmingly, 39% of roads, 43% of farming lands, and 25% of education buildings are observed to lie in the highest flood-prone area. Details on subdistrict level exposures have the potential to serve the decision-makers and planners in site selection for flood management strategies and setting priorities for remedial measures.

中文翻译：

---

基于水文地貌因素多标准分析的孟加拉国三古河流域洪水灾害制图

2019 年三古河流域的洪灾对孟加拉国部分山区的居民、农作物、道路和通讯造成了广泛破坏。绘制洪水灾害地图可以在风险管理中发挥重要作用。为此，本研究评估了 12 个水文地貌因素，即地形湿度指数、海拔、坡度、极端降雨、土地利用和土地覆盖、土壤类型、岩性、曲率、排水密度、坡向、最近距离上方的高度。排水和与溪流的距离。通过单独应用层次分析过程 (AHP) 和分析网络过程 (ANP) 制作的地图的验证分数范围为 0.77 到 0.79。发现在 1 天最大降雨量下基于 ANP 的模型表示可靠的危险图，其精度与现场结果相当。100年重现期灾害图显示，下游71万人口由于地势低洼、坡度平缓、排水密度高，易发生“特高”洪水。令人震惊的是，据观察，39% 的道路、43% 的耕地和 25% 的教育建筑都位于最易发生洪水的地区。分区级暴露的详细信息有可能为决策者和规划者在洪水管理策略的选址和确定补救措施的优先级方面提供服务。据观察，25% 的教育建筑位于最易发生洪水的地区。分区级暴露的详细信息有可能为决策者和规划者在洪水管理策略的选址和确定补救措施的优先级方面提供服务。据观察，25% 的教育建筑位于最易发生洪水的地区。分区级暴露的详细信息有可能为决策者和规划者在洪水管理策略的选址和确定补救措施的优先级方面提供服务。