| 113 | 2 | 47 |
| Downloads | Citas | Reads |
The existence of urban ponding water in the city could greatly affect the daily travel of urban residents and the normal operation of the city under severe weather. Therefore, it was particularly important to find out whether there was urban ponding water in various parts of the city in time. However, in the past, the methods of monitoring urban ponding water were mostly achieved through human feedback, equipment monitoring and other methods with small coverage, high cost and error-prone methods. Some areas of Shenzhen were divided into grids, Shenzhen sliding rainfall data, Shenzhen bus line trajectory data, Shenzhen Water Affairs Bureau waterlogging water level data, and relevant features were extracted. Isolation Forest and Compressed sensing algorithm were used to analyze all the accumulations, the state of urban ponding water at the water monitoring site was estimated, and finally combined with Crowd-sensing, buses were selected to participate in the perception task to collect ponding data to improve the accuracy of the estimation.
[1] 郑宇.城市计算概述[J].武汉大学学报(信息科学版),2015,40(1):1-13.ZHENG Y.Introduction to urban computing[J].Geomatics and information science of Wuhan university,2015,40(1):1-13.
[2] KEHUA S,JIE L,HONGBO F.Smart city and the applications[C]//2011 International Conference on Electronics,Communications and Control.Ningbo:IEEE,2011:1028-1031.
[3] 陈利群，王召森，石炼.暴雨内涝后城市排水规划管理的思考[J].给水排水，2011,37(10):29-33.CHEN L Q,WANG Z S,SHI L.Thinking of urban drainage planning and management after rainstorm and waterlogging[J].Water and wastewater engineering,2011,37(10):29-33.
[4] 吴贤国，王雷，王俊红，等.基于云模型TOPSIS的盾构隧道渗漏水治理方案评价优选研究[J].信阳师范学院学报(自然科学版),2021,34(2):316-323.WU X G,WANG L,WANG J H,et al.Optimization of tunnel leakage treatment scheme based on cloud model TOPSIS[J].Journal of Xinyang normal university (natural science edition),2021,34(2):316-323.
[5] 陈荟慧，郭斌，於志文.移动群智感知应用[J].中兴通讯技术，2014,20(1):35-37.CHEN H H,GUO B,YU Z W.Mobile crowd-sensing application[J].ZTE technology journal,2014,20(1):35-37.
[6] CHEN J Y,YANG J S.Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications[J].Sensors,2019,19(10):2399.
[7] LUDWIG T,REUTER C,PIPEK V.What you see is what I need:mobile reporting practices in emergencies[C]//ECSCW 2013:Proceedings of the 13th European Conference on Computer Supported Cooperative Work.Paphos:springer,2013:181-206.
[8] 徐艺扬，李昆，谢玉静，等.基于GIS的城市内涝影响因素及多元回归模型研究——以上海为例[J].复旦学报(自然科学版),2018,57(2):182-198.XU Y Y,LI K,XIE Y J,et al.Studies on the influencing factors and multiple regression model of urban waterlogging based on GIS—A case of Shanghai,China[J].Journal of fudan university (natural science),2018,57(2):182-198.
[9] CHEN L,FAN X,WANG L,et al.Radar:road obstacle identification for disaster response leveraging cross-domain urban data[C].Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies,2018,1 (4):1-23.
[10] 王慧军，许映秋，谈英姿，等.基于区域网格划分的城市积水预警模型构建[J].机械制造与自动化，2014,43(2):117-120,125.WANG H J,XU Y Q,TAN Y Z,et al.City hydrops forewarning model structure based on city area meshing[J].Machine building and automation,2014,43(2):117-120,125.
[11] 魏军，魏铁鑫，陈莎，等.基于GIS的石家庄市暴雨内涝数学模型与应用[J].干旱气象，2018,36(4):701-708.WEI J,WEI T X,CHEN S,et al.Application of the mathematical model of rainstorm waterlogging in Shijiazhuang City based on GIS[J].Journal of arid meteorology,2018,36(4):701-708.
[12] YAO C F,MA X Q,CHEN B,et al.Distribution forest:an anomaly detection method based on isolation forest[C]//Advanced Parallel Processing Technologies.Cham:Springer,2019:135-147.
[13] PUGGINI L,MCLOONE S.An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data[J].Engineering applications of artificial intelligence,2018,67:126-135.
[14] HOU Y X,DUAN L,QIN J L.Parallel animally detection based on Isolation Forest[J].Computer engineering & science,2017,39(2):236-244.
[15] LIU F T,TING K M,ZHOU Z H.Isolation-based anomaly detection[J].ACM transactions on knowledge discovery from data,2012,6(1):1-39.
[16] KONG L H,XIA M Y,LIU X Y,et al.Data loss and reconstruction in wireless sensor networks[J].IEEE transactions on parallel and distributed systems,2014,25(11):2818-2828.
[17] 邵恒，冯兴乐，包芬.基于深度学习的文本相似度计算[J].郑州大学学报(理学版),2020,52(1):66-71,78.SHAO H,FENG X L,BAO F.Text similarity computation based on deep-learning[J].Journal of Zhengzhou university (natural science edition),2020,52(1):66-71,78.
Basic Information:
DOI：10.13705/j.issn.1671-6841.2021070
China Classification Code:TP18;TU992
Citation Information:
[1]ZHANG Weijie,YU Zhiyong,HUANG Fangwan ,et al.Selection of Opportunistic Sensing Trajectories for Ponding Water Inference[J].Journal of Zhengzhou University(Natural Science Edition),2021,53(04):102-108.DOI:10.13705/j.issn.1671-6841.2021070.
Fund Information:
国家自然科学基金项目(61772136)
2021-03-02
2021
2021-05-25
2021-11-04
2021
1
2021-06-03
2021-06-03
2021-06-03