Please wait a minute...
浙江大学学报(工学版)  2021, Vol. 55 Issue (1): 55-61    DOI: 10.3785/j.issn.1008-973X.2021.01.007
土木工程、交通工程、水利工程     
基于多水文数据关联分析的雨水口异常诊断
陈功1(),郑春华2,翁献明2,HAMEEDBaustani1,胡鸿昊1,马晓宇2,柳景青1,*()
1. 浙江大学 建筑工程学院,浙江 杭州 310058
2. 温州市排水有限公司,浙江 温州 325000
Diagnosis of road drainage inlets’ abnormal condition using multi-hydrological data association analysis
Gong CHEN1(),Chun-hua ZHENG2,Xian-ming WENG2,Baustani HAMEED1,Hong-hao HU1,Xiao-yu MA2,Jing-qing LIU1,*()
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Wenzhou Drainage Limited Company, Wenzhou 325000, China
 全文: PDF(1329 KB)   HTML
摘要:

为了定量探究排水系统的不确定性,基于监测水文数据提出雨水口堵塞程度的评估方法. 通过描述雨水口淹没入流状态下的物理模型,获得监测水文数据与雨水口堵塞面积的关联;计算每次独立积水事件下雨水口平均堵塞面积,作为雨水口堵塞程度评估指标;使用移动平均法对评估指标进行处理,能够直观分析出雨水口的堵塞程度. 将该方法应用于东部沿海城市研究区域,用雨水口清理记录进行验证. 结果表明,有83%研究点位的雨水口堵塞情况可以被很好地鉴别出来,其中诊断为堵塞并被清理的点位准确率为75%,诊断为堵塞但未被清理的点位准确率为89%. 预测失败的点位多由雨水口清理记录不全造成,雨水口侧支管堵塞疏通的延后性会对计算结果造成干扰.

关键词: 城市内涝城市排水系统雨水口堵塞评估时间序列分析    
Abstract:

A method of using monitored hydrological data to assess the degree of inlet blockage was proposed in order to quantitatively analyze the uncertainty of the drainage system. The correlation between the monitored hydrological data and the blocked area of the inlet was obtained by describing the physical model of the road drainage inlet under flooding. The average clogged area of the inlet under each independent water accumulation event was calculated and defined as the index of inlet’s blockage degree. The moving average method was used to process the calculation results in order to visually analyze the degree of inlet’s blockage. The method was applied to an eastern coastal city, and the inlet cleanup records were used to verify the calculation results. Results show that 83% of inlet blockage can be well identified, of which the accuracy of the points diagnosed as cleared is 75%, the accuracy of the points diagnosed as uncleared is 89%. Errors in the calculation results are mostly caused by incomplete records. Some branch pipes may be gradually cleaned up after the inlet is cleared, which interferes with the calculation results.

Key words: urban flooding    urban drainage system    road drainage inlet    blockage diagnosis    time series analysis
收稿日期: 2020-07-29 出版日期: 2021-01-05
CLC:  X 143  
基金资助: 浙江省重大科技专项重大社会发展资助项目(2015C03015)
通讯作者: 柳景青     E-mail: 21812068@zju.edu.cn;liujingqing@zju.edu.cn
作者简介: 陈功(1997—),男,硕士生,从事排水管网异常诊断研究. orcid.org/0000-0003-2765-9756. E-mail: 21812068@zju.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
陈功
郑春华
翁献明
HAMEEDBaustani
胡鸿昊
马晓宇
柳景青

引用本文:

陈功,郑春华,翁献明,HAMEEDBaustani,胡鸿昊,马晓宇,柳景青. 基于多水文数据关联分析的雨水口异常诊断[J]. 浙江大学学报(工学版), 2021, 55(1): 55-61.

Gong CHEN,Chun-hua ZHENG,Xian-ming WENG,Baustani HAMEED,Hong-hao HU,Xiao-yu MA,Jing-qing LIU. Diagnosis of road drainage inlets’ abnormal condition using multi-hydrological data association analysis. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 55-61.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.01.007        http://www.zjujournals.com/eng/CN/Y2021/V55/I1/55

图 1  超声波水位计结构示意图
图 2  水文传感器布设位置
序号 积水位置 积水时间 深度/cm 面积/m2 积水原因 处置措施
1 (120.815350,27.860610) 2019-10-01 08:42:26 5 80 雨水篦子支管堵塞 疏通管道
2 (119.821000,33.989999) 2019-10-01 08:00:43 5 50 树叶垃圾堵塞 清通雨水篦子
3 (120.794699,27.796859) 2019-09-30 21:18:15 5 50 黄泥堵塞 清通雨水篦子
$\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $
270 (120.547630,27.984948) 2019-09-02 20:42:01 10 50 堵塞 开井疏通
表 1  2019年6月—10月洪水干预运维记录
图 3  传感器收集降雨量路面积水数据示例
图 4  雨水口健康诊断技术路线
图 5  雨水口泄流形式的示意图
图 6  积水事件持续时间占比情况
图 7  雨水口堵塞严重程度计算结果示例
图 8  有清理记录点位预测清理区间与真实清理时间对比
图 9  雨水口堵塞严重程度未修正计算结果示例
1 HENONIN J, RUSSO B, MARK O, et al Real-time urban flood forecasting and modelling: a state of the art[J]. Journal of Hydroinformatics, 2013, 15 (3): 717- 736
doi: 10.2166/hydro.2013.132
2 PALLA A, COLLI M, CANDELA A, et al Pluvial flooding in urban areas: the role of surface drainage efficiency[J]. Journal of Flood Risk Management, 2018, 11: S663- S676
doi: 10.1111/jfr3.12246
3 GóMEZ M, RABASSEDA G H, RUSSO B Experimental campaign to determine grated inlet clogging factors in an urban catchment of Barcelona[J]. Urban Water Journal, 2013, 10 (1): 50- 61
doi: 10.1080/1573062X.2012.690435
4 ARONICA G T, LANZA L G Drainage efficiency in urban areas: a case study[J]. Hydrological Processes, 2005, 19 (5): 1105- 1119
doi: 10.1002/hyp.5648
5 LEIT?O J P, SIM?ES N E, PINA R D, et al Stochastic evaluation of the impact of sewer inlets’ hydraulic capacity on urban pluvial flooding[J]. Stochastic Environmental Research and Risk Assessment, 2017, 31 (8): 1907- 1922
doi: 10.1007/s00477-016-1283-x
6 DEL GIUDICE D, HONTI M, SCHEIDEGGER A, et al Improving uncertainty estimation in urban hydrological modeling by statistically describing bias[J]. Hydrology and Earth System Sciences, 2013, 17 (10): 4209- 4225
doi: 10.5194/hess-17-4209-2013
7 周云峰, 周永潮, 郑春华, 等 采用Sobol方法的暴雨径流管理模型参数灵敏度分析[J]. 浙江大学学报: 工学版, 2019, 53 (2): 347- 354
ZHOU Yun-feng, ZHOU Yong-chao, ZHENG Chun-hua, et al Sensitivity analysis of parameters of storm water management model with Sobol method[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (2): 347- 354
8 PINA R, OCHOA-RODRIGUEZ S, SIM?ES N, et al Semi- vs. fully-distributed urban stormwater models: model set up and comparison with two real case studies[J]. Water, 2016, 8 (2): 58
doi: 10.3390/w8020058
9 KOLSKY P, BUTLER D Performance indicators for urban storm drainage in developing countries[J]. Urban Water, 2002, 4 (2): 137- 144
doi: 10.1016/S1462-0758(02)00011-0
10 CHERQUI F, BELMEZITI A, GRANGER D, et al Assessing urban potential flooding risk and identifying effective risk-reduction measures[J]. Science of the Total Environment, 2015, 514: 418- 425
doi: 10.1016/j.scitotenv.2015.02.027
11 CHEN Y, ZHOU H, ZHANG H, et al Urban flood risk warning under rapid urbanization[J]. Environmental Research, 2015, 139: 3- 10
doi: 10.1016/j.envres.2015.02.028
12 FRIES K J, KERKEZ B Using sensor data to dynamically map large-scale models to site-scale forecasts: a case study using the national water model[J]. Water Resources Research, 2018, 54 (8): 5636- 5653
doi: 10.1029/2017WR022498
13 AGGARWAL A, RAFIQUE F, RAJESH E, et al Urban flood hazard mapping using change detection on wetness transformed images[J]. Hydrological Sciences Journal, 2016, 61 (5): 816- 825
14 ROSSER J F, LEIBOVICI D G, JACKSON M J Rapid flood inundation mapping using social media, remote sensing and topographic data[J]. Natural Hazards, 2017, 87: 103- 120
doi: 10.1007/s11069-017-2755-0
15 FRENCH R The non-impact of Debris blockages on the August 1998 Wollongong flooding[J]. Australian Journal of Water Resources, 2012, 15 (2): 161- 169
16 陈倩, 夏军强, 董柏良 城市洪涝中雨水口泄流能力的试验研究[J]. 水科学进展, 2020, 31 (1): 10- 17
CHEN Qian, XIA Jun-qiang, DONG Bo-liang Experimental study on discharge capacity of street inlet in urban flooding[J]. Advances in Water Science, 2020, 31 (1): 10- 17
[1] 魏媛,冯天恒,黄平捷,侯迪波,张光新. 管网水质多指标动态关联异常检测方法[J]. 浙江大学学报(工学版), 2016, 50(7): 1402-1409.
[2] 王智磊,孙红月,刘永莉,尚岳全. 降雨与边坡地下水位关系的时间序列分析[J]. J4, 2011, 45(7): 1301-1307.