RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

Urban drainage pipe network is the backbone of urban drainage,flood control and water pollution prevention,and is also an essential symbol to measure the level of urban modernization.A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age.The repair of drainage pipe networks has attracted extensive attention from all walks of life.Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019,various provinces in the Yangtze River Basin,such as Anhui,Jiangxi and Hunan,have extensively carried out PPp projects for urban pipeline restoration,in order to improve the quality and efficiency of sewage treatment.Based on the management practice of urban pipe network restoration project in Wuhu City,Anhui Province,this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode,and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine.The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice.The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes,and its accuracy can reach 90%.The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

Research on numerical simulation of the groundwater funnels restoration in Shijiazhuang

In order to simulate the recovery of groundwater funnels under the condition of reducing groundwater abstraction, hydrogeological conditions of recoverability construction of Shijiazhuang groundwater funnel were analyzed, and a numerical simulation method was used based on the change of various parameters. The results show that the groundwater flow model can accurately reflect groundwater hydrogeological characteristics, and can guarantee the reliability of groundwater restoration prediction. The research has set up four schemes for rural water-saving, planting structure adjustment, urban reducing abstraction and integrated method. The effect of four restoration schemes on the restoration of groundwater funnels was compared with water table variations of two observation well. Comparison results show that the level changes of plan Three and Four are different from the other two kinds of exploitation and the drop trend of water table in the funnel area is flat. So we can conclude that Plan Three and Four have significant effect on the groundwater funnel restoration of Shijiazhuang.

Cloud-Data Envelopment Analysis Method Used for Assessment of Restoration Building Block Schemes

The selection of an optimized restoration building block(RBB)scheme among all available schemes is one of the most important factors impacting the power system restoration process after a complete or partial blackout.This paper presents a data envelopment analysis(DEA)model used as an empirical method to assess the RBB schemes.An N-level evaluation scale cloud system is built based on cloud theory to transform qualitative I/O indices of DEA model into quantitative values.Through joint utilization of the CCR(Charnes,Cooper and Rhodes)model and the LJK(Li,Jahanshahloo and Khodabakhshi)model,the established Joint-DEA model makes the newly proposed Cloud-DEA method a more feasible and robust method in assessment of RBB schemes.