[1]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.[doi:10.3724/SP.J.1249.2021.01027]
　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]
点击复制

基于Kriging插值的校园内涝模拟与模型优选()
分享到：

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第38卷
期数:
2021年第1期
页码:
27-35
栏目:
专题：海绵城市
出版日期:
2021-01-12

文章信息/Info

Title:
Campus waterlogging simulation and model preference based on Kriging interpolation
文章编号:
202101004
作者:
杨亚红1; 杨兴峰1; 闫俊江1; 蔚阳2
1）兰州理工大学土木工程学院,甘肃兰州730050
2）清华大学深圳研究生院，广东深圳518055
Author(s):
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
关键词:
海绵城市; Kriging插值; 半变异模型; 交叉验证; 拟合模型; 径流; 低影响开发; 内涝
Keywords:
sponge city;  Kriging interpolation;  semi-variation model;  cross-validation;  fitting model;  radial flow;  low impact development (LTD);  waterlogging
分类号:
X328
DOI:
10.3724/SP.J.1249.2021.01027
文献标志码:
A
摘要:
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．
Abstract:
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.

参考文献/References:

［1］ CAI Wenjun, ZHU Xueping, PENG Anbang, et al. Flood risk analysis for cascade dam systems: a case study in the Dadu River Basin in China［J］. Water, 2019, 11(7): 21-32.
［2］ 任建超, 谢水波, 刘慧, 等. 海绵城市背景下的城市内涝防控策略研究［J］. 水利规划与设计, 2020(11): 35-38,105.
REN Jianchao, XIE Shuibo, LIU Hui, et al. Research on urban waterlogging prevention and control strategy under the background of sponge city［J］. Water Resources Planning and Design,2020(11): 35-38,105.(in Chinese)
［3］ BENITO G, LANG M, BARRIENDOS M, et al. Use of systematic, palaeoflood and historical data for the improvement of flood risk estimation: review of scientific methods［J］. Natural Hazards, 2004, 31(3): 623-643.
［4］ WASHAKH R M A, CHEN Ningsheng, WANG Tao, et al. GLOF risk assessment model in the himalayas: a case study of a hydropower project in the upper Arun River［J］. Water, 2019, 11(9): 1-23.
［5］ MUHAMMAD A, EVENSON G R, STADNYK T A, et al. Impact of model structure on the accuracy of hydrological modeling of a Canadian Prairie watershed［J］. Journal of Hydrology: Regional Studies, 2019, 21: 40-56.
［6］ 尹占娥, 许世远, 殷杰, 等. 基于小尺度的城市暴雨内涝灾害情景模拟与风险评估［J］. 地理学报, 2010, 65(5): 553-562.
YIN Zhan’e, XU Shiyuan, YIN Jie, et al. Simulation and risk assessment of urban rainstorm and waterlogging disaster based on small scale［J］. Acta Geographica Sinica, 2010, 65(5): 553-562.(in Chinese)
［7］ 胡蓓蓓, 周俊, 王军, 等. 基于情景模拟的天津市滨海新区 2020 年暴雨内涝风险评估［J］. 地理科学, 2012, 32(7): 846-852.
HU Peipei, ZHOU Jun, WANG Jun, et al. Risk assessment of rainstorm waterlogging in binhai new area of tianjin in 2020 based on scenario simulation［J］. Acta Geographica Sinica, 2012, 32(7): 846-852.(in Chinese)
［8］ 刘春春, 刘万青, 张悦, 等. 基于SWMM模型的西安市清河流域暴雨洪峰流量模拟［J］. 干旱区研究, 2018, 35(1): 35-42.
LIU Chunchun, LIU Wanqing, ZHANG Yue, et al. Simulation of rainstorm peak discharge in qinghe river basin of Xi’an city based on SWMM model［J］. Arid Zone Research, 2018, 35(1): 35-42.(in Chinese)
［9］ 闫星光, 吴琳娜, 周涌, 等. 喀斯特地区月均降水协克里金插值方法研究——以贵州省为例［J］. 云南大学学报自然科学版, 2017, 39(3): 432-439.
YAN Xingguang, WU Linna, ZHOU Yong, et al. Study on co-Kriking interpolation method for monthly mean precipitation in karst region: a case study of Guizhou province［J］. Journal of Yunnan University Natural Sciences Edition, 2017, 39(3): 432-439.(in Chinese)
［10］ 刘永坤, 陈放, 汤春节, 等. 顾及各向异性的三维克里金空间插值［J］. 科技通报, 2019(4): 7-15.
LIU Yongkun, CHEN Fang, TANG Chunjie, et al. Three-dimensional Kriging interpolation with anisotropy in mind［J］. Bulletin of Science and Technology, 2019(4): 7-15.(in Chinese)
［11］ 陈昭, 罗小波, 高阳华, 等. 基于半变异函数的重庆市地表温度空间异质性建模及多尺度特征分析［J］. 地球信息科学学报, 2019, 21(7): 1051-1060.
CHEN Zhao, LUO Xiaobo, GAO Yanghua, et al. Modeling and multi-scale analysis of the spatial heterogeneity of land surface temperature in Chongqing based on Semi-Variogram［J］. Journal of Geo-Information Science, 2019, 21(7): 1051-1060.(in Chinese)
［12］ QIN Qianqian, WANG Haiyan, LEI Xiangdong, et al. Spatial variability in the amount of forest litter at the local scale in northeastern China: Kriging and cokriging approaches to interpolation［J］. Ecology and Evolution, 2020, 10(2):778-790.
［13］ 王杰, 杨银, 吴红, 等. 兰州市暴雨强度公式拟合方法研究［J］. 高原山地气象研究, 2016, 36(4): 23-27.
WANG Jie, YANG Yin, WU Hong, et al. Study on fitting method for rainstorm intensity formula in Lanzhou［J］. Plateau and Mountain Meteorology Research, 2016, 36(4): 23-27.(in Chinese)
［14］ LI Jiake, DENG Chenning, LI Huaien, et al. Hydrological environmental responses of LID and approach for rainfall pattern selection in precipitation data-lacked region［J］. Water Resources Management, 2018, 32(10): 3271-3284.
［15］ ZAKHAROVA E A, KOURAEV A V, STEPHANE G, et al. Recent dynamics of hydro-ecosystems in thermokarst depressions in Central Siberia from satellite and in situ observations: importance for agriculture and human life［J］. Science of the Total Environment, 2018, 615: 1290-1304.
［16］ GAAFAR M, MAHMOUD S H, GAN T Y, et al. A practical GIS-based hazard assessment framework for water quality in stormwater systems［J］. Journal of Cleaner Production, 2020, 245:1-38.

［17］ 刘海文, 丁一汇. 华北汛期日降水特性的变化分析［J］. 大气科学, 2010, 34(1): 12-22.
LIU Haiwen, DING Yihui. Analysis of daily precipitation characteristics over North China during rainy seasons［J］. Chinese Journal of Atmospheric Sciences, 2010, 34(1): 12-22.(in Chinese)
［18］ 彭竹葳. 基于 SWMM 的滇池环湖截污干渠(东岸段)的截流能力及运行状况分析［D］. 重庆：重庆大学, 2014.
PENG Zhuwei. Study on intercepting capability and operational state of interception Trunk (Eastern Section) of the Dianchi Lake based on SWMM［D］. Chongqing: Chongqing University, 2014.(in Chinese)
［19］ 季青, 余明. 基于协同克里格插值法的年均温空间插值的参数选择研究［J］. 首都师范大学学报自然科学版,2010,31(4):81-87.
JI Qing, YU Ming. Study on parameters setting of ordinary cokriging interpretation to average annual temperature［J］, 2010,31(4):81-87.(in Chinese)

相似文献/References:

[1]陈飞勇.利用自然力量建设“海绵城市”专题研究序言[J].深圳大学学报理工版,2020,37(4):331.[doi:10.3724/SP.J.1249.2020.04331]
　CHEN Feiyong.Preface to the special research on building the sponge city by natural power[J].Journal of Shenzhen University Science and Engineering,2020,37(1):331.[doi:10.3724/SP.J.1249.2020.04331]
[2]刘建,龚小强,任心欣,等.深圳市海绵城市的建设与创新[J].深圳大学学报理工版,2020,37(4):334.[doi:10.3724/SP.J.1249.2020.04334]
　LIU Jian,GONG Xiaoqiang,REN Xinxin,et al.Sponge city construction and innovation in Shenzhen[J].Journal of Shenzhen University Science and Engineering,2020,37(1):334.[doi:10.3724/SP.J.1249.2020.04334]
[3]刘安,李玉清,郭奇聪,等.深圳典型道路原位模拟雨水生物毒性评价分析[J].深圳大学学报理工版,2020,37(4):355.[doi:10.3724/SP.J.1249.2020.04355]
　LIU An,LI Yuqing,GUO Qicong,et al.Biotoxicity assessment of in-situ simulated stormwater runoff on typical urban roads in Shenzhen[J].Journal of Shenzhen University Science and Engineering,2020,37(1):355.[doi:10.3724/SP.J.1249.2020.04355]
[4]彭世瑾,胡爱兵,史敬华,等.基于深圳典型片区海绵城市监测评估的思考[J].深圳大学学报理工版,2021,38(1):1.[doi:10.3724/SP.J.1249.2021.01001]
　PENG Shijin,HU Aibing,SHI Jinghua,et al.Reflection on the sponge city monitoring and evaluation in Shenzhen[J].Journal of Shenzhen University Science and Engineering,2021,38(1):1.[doi:10.3724/SP.J.1249.2021.01001]
[5]吴亚男,孔露霆,任心欣,等.海绵城市排水分区绩效达标规划实施路径探索——以深圳市国家试点区域某排水分区为例[J].深圳大学学报理工版,2021,38(1):10.[doi:10.3724/SP.J.1249.2021.01010]
　WU Yanan,KONG Luting,REN Xinxin,et al.Exploration on a planning and implementation method of reaching the sponge city performance standard at catchment level: taking a catchment in national pilot area of Shenzhen as an example[J].Journal of Shenzhen University Science and Engineering,2021,38(1):10.[doi:10.3724/SP.J.1249.2021.01010]
[6]刘建,刘派,王晓雷,等.深圳地铁长圳车辆段海绵城市设施设计与施工[J].深圳大学学报理工版,2021,38(1):20.[doi:10.3724/SP.J.1249.2021.01020]
　LIU Jian,LIU Pai,WANG Xiaolei,et al.Design and construction of sponge city facilities in Changzhen depot of Shenzhen metro[J].Journal of Shenzhen University Science and Engineering,2021,38(1):20.[doi:10.3724/SP.J.1249.2021.01020]
[7]李笑玥,秦华鹏,王凡,等.生物滞留池微生物种群的硝化反硝化功能研究——以深圳市为例[J].深圳大学学报理工版,2021,38(1):36.[doi:10.3724/SP.J.1249.2021.01036]
　LI Xiaoyue,QIN Huapeng,WANG Fan,et al.Nitrification and denitrification functions of microbial population in bioretention cells: a case study of Shenzhen[J].Journal of Shenzhen University Science and Engineering,2021,38(1):36.[doi:10.3724/SP.J.1249.2021.01036]
[8]蔡业钊,刘东煊,刘建,等.基于实地监测的新型粒状海绵介质土的性能研究[J].深圳大学学报理工版,2021,38(1):45.[doi:10.3724/SP.J.1249.2021.01045]
　CAI Yezhao,LIU Dongxuan,LIU Jian,et al.Performance of innovative granular sponge city growing media based on field monitoring[J].Journal of Shenzhen University Science and Engineering,2021,38(1):45.[doi:10.3724/SP.J.1249.2021.01045]
[9]冉杨涛,吕伟娅,袁校柠.水资源综合利用专项规划评估体系构建及应用[J].深圳大学学报理工版,2021,38(4):358.[doi:10.3724/SP.J.1249.2021.04358]
　RAN Yangtao,LV Weiya,and YUAN Xiaoning.Establishment and application of special plan evaluation system for comprehensive utilization of water resources[J].Journal of Shenzhen University Science and Engineering,2021,38(1):358.[doi:10.3724/SP.J.1249.2021.04358]

备注/Memo

备注/Memo:
Received：2020-05-29；Accepted：2020-09-18
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）
基金项目：甘肃省科技计划资助项目（1506RJZA099）
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