摘要
由于对地形地貌特征、临界雨量、汇流时间和重现期等洪水特征因子的认识不足,干旱半干旱地区中小流域的洪水预报准确率有待提高。因此,本文选取甘肃省夏河县(位于半干旱区)和永昌县(位于干旱区)为研究对象,对夏河县(2015年)86个沿河村落的34个小流域和永昌县(2014年)395个沿河村落的240个过水断面的洪水特征要素进行了野外调查,并采用“铁一院”法、瞬时单位线法和地区经验公式法计算了两个区域的汇流时间、设计暴雨和设计洪水,推算了洪水预警值。然后基于计算结果,采用机器学习方法(线性回归、随机森林和神经网络),建立了针对干旱和半干旱地区的洪水预警模型,并对各模型进行了评价分析。结果表明,准备转移雨量与暴雨时程分配、临界雨量和主沟比降之间存在线性相关性,且线性回归模型能较准确地计算预警降雨量。为进一步验证回归模型适用性,使用该模型反演分析甘肃省合作市34个调查流域的准备转移雨量,平均绝对误差仅为0.56 mm。
The accuracy of flood forecasting in small and medium-sized basins in arid and semi-arid regions needs to be improved,pri⁃marily due to a limited understanding of critical factors such as topographic features,critical rainfall,time of concentration,and recur⁃rence intervals.In this paper,Xiahe County,Gansu Province,which is located in the semi-arid region,and Yongchang County,Gansu Province,which is located in the arid region,are selected as the research objects.Field investigations were carried out on flood-related factors across 34 small basins in 86 riverine villages of Xiahe County(2015)and 240 cross-sections in 395 riverine villages of Yongchang County(2014).Flood characteristics were assessed using the instantaneous unit hydrograph method,the regional empirical formula method,and the method proposed by the China Railway First Survey and Design Institute Group Co.,Ltd.These methods were employed to calculate the time of concentration,design storms,and design floods for the study areas,and flood warning thresholds were estimated.Based on the calculation results,machine learning methods(linear regression,random forest and neural network)were used to establish a flood warning model for arid and semi-arid areas,and each model was evaluated and analyzed.The results show that there is a linear correlation between prepared transfer rainfall and factors such as rainfall during storms,warning rainfall threshold,and main channel slope,and the linear regression model can accurately calculate the warning rainfall.In order to further verify the applicability of the regression model,the model is used to invert and analyze the prepared transfer rainfall of 34 survey basins in Hezuo City,Gansu Province,the mean absolute error is only 0.56 mm.
作者
苏宏梅
张楠
冉新民
康超
SU Hongmei;ZHANG Nan;RAN Xinmin;KANG Chao(Jinchang City River and Lake Management Center of Gansu Province,Jinchang 737100,Gansu,China;College of Earth and Environmental Sciences,Lanzhou University,Lanzhou 730000,China;Zhejiang Zhongshui Engineering Technology,Co.Ltd,Lanzhou office,Lanzhou 730000,China)
出处
《干旱气象》
2024年第5期683-693,共11页
Journal of Arid Meteorology
基金
甘肃省自然科学基金项目(23JRRA664)资助。
关键词
干旱半干旱区
洪水
暴雨
中小尺度
机器学习
arid and semi-arid regions
flood
heavy rain
meso-micro scale
machine learning