环境科学
雨水收集
大洪水
水文学(农业)
雨水
水资源管理
地表径流
气象学
地理
地质学
生态学
生物
考古
岩土工程
作者
Yazhen Jiang,Anchen Qin
标识
DOI:10.1016/j.pce.2023.103487
摘要
Research on stormwater inundation risk and rainwater management in scenic areas has a lot to do with rainfall during the flood season. When the measured rainfall data is limited, an artificial network model with nonlinear mapping capability can be applied to predict rainfall data during the flood season, which increases the sample size of rainfall data and improves the accuracy of research results. Based on a radial basis function (RBF)neural network model, this paper takes the Yesanpo Scenic Area in Baoding City, Hebei Province as an example to estimate the monthly maximum rainfall data during the flood season (July–September) of 2022, 2023, and 2024 in the study area. On this basis, the Pearson III frequency curve is used to calculate the design rainfall corresponding to the rainfall frequency of 20%, 5%, and 2%. With the help of SCS-CN model and ArcGIS spatial analysis tools, the stormwater inundation areas are simulated in the study area, which are divided into three risk levels: high, medium, and low, providing a reference for the stormwater management in the Yesanpo Scenic Area.
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