YANG Yahong,YANG Xingfeng,YAN Junjiang,et al.Campus waterlogging simulation and model preference based on Kriging interpolation[J].Journal of Shenzhen University Science and Engineering,2021,38(1):27-35.[doi:10.3724/SP.J.1249.2021.01027]





Campus waterlogging simulation and model preference based on Kriging interpolation
YANG Yahong1 YANG Xingfeng1 YAN Junjiang1 and YU Yang2
1) College of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu Province, P.R.China
2)Shenzhen Graduate School of Tsinghua University, Shenzhen 518055, Guangdong Province, P.R.China
sponge city Kriging interpolation semi-variation model cross-validation fitting model radial flow low impact development (LTD) waterlogging
为探索中国兰州市某高校校园雨水管网节点积水深度的主要影响因子,以研究区域雨水管网246个节点在降雨重现期(P)为7 a和50 a时积水深度的数据为例,利用ArcGIS统计模块分析2个重现期降雨下的节点积水深度数据的空间差异性,采用交叉验证法对比研究4种Kriging插值模型(稳定、球形、高斯与指数模型).由Pearson相关性分析结果可知,2种重现期的积水情形下节点最大深度与积水深度相关性均较强,相关系数分别为0.605和0.766. 4种Kriging半变异函数模型的预测值与实测值的对比结果表明,高斯模型的偏差均值(Kriged reduced mean error, KRME)最小(P=7 a时,KRME=-0.87×10-4;P=50 a时,KRME=0.87×10-3),一致性系数(Kriged reduced mean square error, KRMSE)最优(P=7 a时,KRMSE=0.939;P=50 a时,KRMSE=0.947),确定该研究区域节点积水深度Kriging插值方法最适宜的模型为高斯模型.研究结果可为利用研究区域有限的内涝数据更有效地识别积水内涝提供方法,同时为内涝的控制和消减措施提供理论基础.
In order to explore the main influence factors of the node water depth of rainwater pipe network located in a campus in Lanzhou city and to study the interpolation method suitable for the node water depth data, we analyze the data of the water depth of 246 nodes of the rainwater pipe network in the study area under the rainfall return periods of 7 years and 50 years (i.e., P=7 a and P=50 a, respectively). Using ArcGIS software, we make spatial difference analysis of node water depth data of rainfalls in the two return periods, and conduct a comparative study on the four Kriging interpolation models, i.e. stable model, spherical model, Gaussian model and index model, by means of cross-validation method. The results show that the Pearson correlations between the maximum depth of nodes and the depth of water accumulation for P=7 a and P=50 a are strong, and the correlation coefficients are 0.605 and 0.766, respectively. Through comparing the predicted values with the measured values of four Kriging semi-variation function models, it is found that the Kriged reduced mean error by Gaussian model is minimum (KRME=-0.87×10-4 at P=7 a and KRME=0.87×10-3 at P=50 a), and the Kriged reduced mean square error is the best (KRMSE=0.939 at P=7 a and KRMSE=0.947 at P=50 a). The Gaussian model is determined as the most suitable model for Kriging interpolation method of node water depth in the study area, which provides a more effective method to identify water logging in the study area by using limited waterlogging data, and provides a theoretical basis for the control and reduction measures of waterlogging.


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Foundation:Science and Technology Program of Gansu Province(1506RJZA099)
Corresponding author:Associate professor YANG Yahong. E-mail:yangyahong@Lut.cn
Citation:YANG Yahong, YANG Xingfeng, YAN Junjiang, et al. Campus waterlogging simulation and model preference based on Kriging interpolation[J]. Journal of Shenzhen University Science and Engineering, 2021, 38(1): 27-35.(in Chinese)
作者简介:杨亚红(1976—),兰州理工大学副教授、博士.研究方向:水质净化机理. E-mail: yangyahong@Lut.cn
引文:杨亚红,杨兴峰,闫俊江,等.基于Kriging插值的校园内涝模拟与模型优选[J]. 深圳大学学报理工版,2021,38(1):27-35.
更新日期/Last Update: 2021-01-26