深圳大学学报理工版

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.

引言
1 材料和方法
2 结果与分析
3 结论

图1 半变异函数拟合曲线示例[12]<br/>Fig.1 Examples of semi-variation fitting curves[12]

图1 半变异函数拟合曲线示例[12]
Fig.1 Examples of semi-variation fitting curves[12]

图2 研究区域范围(线内)<br/>Fig.2 The study area(in line)

图2 研究区域范围(线内)
Fig.2 The study area(in line)

图3 研究区域概化结果<br/>Fig.3 The regional generalization results of study area

图3 研究区域概化结果
Fig.3 The regional generalization results of study area

图4 不同重现期下芝加哥降雨过程线<br/>Fig.4 The precipitation pattern of Chicago in different recurrence periods

图4 不同重现期下芝加哥降雨过程线
Fig.4 The precipitation pattern of Chicago in different recurrence periods

表1 实测降雨数据<br/>Table 1 Measured rainfall data

表1 实测降雨数据
Table 1 Measured rainfall data

表2 节点积水深度与各影响因子的Pearson相关性系数<br/>Table 2 Pearson correlation coefficients of node water depth and influence factors

表2 节点积水深度与各影响因子的Pearson相关性系数
Table 2 Pearson correlation coefficients of node water depth and influence factors

图5 积水点数据概括性统计分析<br/>Fig.5 Summary statistical analysis of water accumulation point data

图5 积水点数据概括性统计分析
Fig.5 Summary statistical analysis of water accumulation point data

表3 四种待选模型参数对比<br/>Table 3 Comparison of parameters of four models to be selected

表3 四种待选模型参数对比
Table 3 Comparison of parameters of four models to be selected

图6 交叉验证结果<br/>Fig.6 The result of cross-validation

图6 交叉验证结果
Fig.6 The result of cross-validation

图7 节点积水深度Kriging高斯模型插值结果<br/>Fig.7 Interpolation results of Kriging Gaussian model for node water depth

图7 节点积水深度Kriging高斯模型插值结果
Fig.7 Interpolation results of Kriging Gaussian model for node water depth

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备注

引言

1 材料和方法

2 结果与分析

3 结论

期刊信息

备注

引言

1 材料和方法

2 结果与分析

3 结 论

期刊信息

3 结论