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

The core of comprehensive treatment effect of water environment in river basin lies in pipe network system. The blockage of urban drainage pipe network is one of the main reasons affecting the healthy operation of urban drainage network system. So the intelligent analysis method of urban drainage pipe network blockage diagnosis can quickly find the problems of urban drainage pipe network and improve the operation and maintenance efficiency of urban drainage pipe network. In this paper, through the analysis of rainfall data, rainfall and sewage pipe network flow variation regular pattern, the change range, the essay obtains the rain and sewage pipe network blockage diagnosis threshold. Combined with the urban intelligent drainage pipe network system, through the customized setting of pipe network blockage threshold, the rapid diagnosis and early warning of the blockage problem of the drainage pipe network are realized, and the healthy operation of the urban drainage network is guaranteed.

Based on the one-dimensional channel conservancy model, this paper considers the factor of urban pipe network drainage, and couples the pipe network model with the one-dimensional channel conservancy model to fully analyse the interaction between the channel water level and the drainage pipe network displacement. Taking a coastal city as an example, the model simulation results show that when the water level of the river is high, it will cause the drainage of the pipe network; the lower part of the terrain will become waterlogged because of the aging of the urban pipe and rainstorm. It is impossible to effectively solve the problem of flood control and drainage by widening the river channel. It is necessary to adopt the coordinated improvement of the pipe network and the river channel to achieve effective targets.

Despite urban drainage pipe networks being of great significance to urban flood control, few studies have focused on the influence of urban drainage pipe network structure on hydrological response. We studied two areas in downtown Dongying, Shandong Province, China, which have dendritic and loop drainage pipe networks, respectively. The loop network was converted to a dendritic system for scenario analysis using numerical simulation. Comparing the hydrological response of the original drainage system and the converted system showed that although the dendritic network could reduce node flood volume, the dendritic pipe overload situation was inferior to the loop network. Runoff in the former network was predominantly concentrated in the main pipes and near the outlets and the discharge-peak at the outlet was 47.0% greater than that of the loop. Thus, the loop drainage pipe network is more likely to reduce peak runoff at the outlet, which helps decrease flood risk.

Research on intelligent control theory and strategy of gas drainage pipe network based on graph theory

The urban underground pipe network plays a vital role in the city development, the construction of unreasonable urban pipe networks restricts the city development, even has a disastrous effect on the city. However, it is not desirable to overstate the investment of the urban underground pipe network excessively. It is an important subject of the city construction that how to establish reasonable urban pipe networks to meet service conditions of underground pipe networks. Because the construction of underground drainage pipe networks of most of the major cities in our country mainly comes from the 50s of last century and there are more pipe network system with the development of the city, it is the main content of our research that how to reform and construct them under the condition of existing underground pipe networks.

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

In drainage engineering design, particularly, in analysis, check and hydraulic calculation of drainage pipe network, recognition and acquisition of drainage pipe network is the first and foremost task (Li et al., 2007).To implement the auto-generation of drainage pipe network drawing, this paper applies the graph theory to model the composition and data structure of a drainage pipe network. An effective method is proposed to automatically generate the directed drainage pipe network given a sink. The Breadth-first Search algorithm, Incidence-edge calculation algorithm and Transpose-graph algorithm from Boost Graph Library (BGL) are applied to implement the algorithm for the proposed method. The method provides greater automation and more efficiency but requires less inputs in the process of obtaining the practical drainage pipe network than other commonly used software do. It also lays a foundation for drainage pipe network analysis and related to hydraulic calculations. The accuracy and efficiency of the method is verified by the engineering practices described in this paper.

With the rapid development of urbanization, impervious areas spread quickly, which have greatly influenced regional hydrology and water resources. The water quality is getting worse and worse. The flood and drought disasters have aggravated. Human living environment has changed and consequently sustainable development of economy is restricted. The drainage pipe network plays a very important role in urban drainage and flood control. In the study, using of computer technology, the drainage capacity of study area in different design rainstorm return periods (P = 0.25, 0.5, 1, 2, 5, 10 a, respectively) is analyzed and simulated by Storm Water Management Model (SWMM). Part of the pipe network that are filled slowly and change great are found out fewer than 6 design rainstorms, which provide the basis for reconstruction of urban conduits. The simulation results can provide strong technical support for the city drainage system planning and management.

Urban rainwater pipe network is a direct way to resist urban waterlogging, and its safe and stable operation is the premise to ensure the normal operation of the city. However, in China and even around the world, the monitoring means for the operation of urban rainwater pipe network are very limited. For the occurrence and solution of urban waterlogging, it often mainly depends on the means of personnel inspection to monitor where the rainwater pipe network overflows, and then take measures. There is no effective monitoring method for the whole rainwater pipe network. Therefore, an accurate and efficient rainwater pipe network monitoring method is urgently needed. To solve the current problem of real-time monitoring of rainwater pipe network, the core problem is how to monitor the liquid level data of any pipe section of the whole rainwater pipe network in real time, so it is necessary to introduce hydrodynamic model. The traditional hydrodynamic model mainly simulates the hydraulic and hydrological, and does not simulate the rainwater pipe network, but makes a generalized estimation of the drainage of the rainwater pipe network. In addition, some hydrodynamic models for drainage pipe network are not established for the problem of rainwater pipe network monitoring. The ultrasonic liquid level gauge with real-time acquisition function is not introduced into the calculation simulation, and it does not have the ability of adaptive correction. Therefore, the traditional rainwater monitoring methods and hydrodynamic models can not fundamentally solve the problem of real-time monitoring of the whole rainwater pipe network. Using hydrodynamic model calculation, combined with rain gauge and liquid level gauge to monitor and modify model parameters, so as to intelligently monitor the whole rainwater pipe network, which can greatly improve the current monitoring level of urban rainwater pipe network and reduce the work cost of manual patrol monitoring.

With the development of the simulative theory and calculation method of drainage pipe network, urban drainage pipe network model gradually become an indispensable part of the urban drainage system management. The characteristics of modern drainage pipelines,the content of drainage pipelines system model, the problems of combining GIS with drainage pipelines and how to solve them are briefly described in this paper.

Numerical simulation study on the effect of underground drainage pipe network in typical urban flood

Urban waterlogging control: A novel method to urban drainage pipes reconstruction, systematic and automated

The prediction of urban water demand is likely to play a significant role in the planning of urban water supply and drainage pipe network. The traditional water demand forecasting methods, including the water index method and the fuzzy water demand prediction model, are widely used with some deficiencies. The water demand prediction results varied among different water quantity indexes with subjectivity. The prediction results by fuzzy water demand prediction model, with fuzzy model structure, are lack of spatial distribution. Meanwhile, the process of urban water demand is closely related to the land use of urban unit. The difference of intensity and characteristic of water demand is obvious among different land use types. In this paper, a distributed urban water demand prediction model was established based on the relationship of water demand process, considering of the eight types of urban construction land use data and three types of major water demand data. In this model, we assumed the linear relationship between water demand and land use unit, ignored the fire water demand because with more randomness than other water demand process. The distributed model for urban water demand prediction was applied in Xiamen. We obtained the land use data and water demand data in 2004–2014 of Xiamen from official statistics. The monolayer construction area and the number of building stories in the residential units obtained by the high definition satellite image and the actual measurement were used to calculated the area of residential land unit, while the area of other land unit was obtained from official land planning in 2020 of Xiamen. The water demand parameters of every urban construction land unit were calibrated by PEST, and the rationality of parameters was analyzed. We observed the stability of the water demand parameters in the model while the results showed great similarity with different calibration period in the same validation period. The water demand of Xiamen and its spatial distribution in 2020 were predicted by the distributed water demand prediction model. The results showed that the water demand of Xiamen in 2020 will reach to 366.57 million tons, which will increase by 24.2% than that in 2014; the water demand intensity of residential land and industrial land will be greater than other types of construction land; the water demand intensity of residential land in Xiamen Island will be greater than the other residential land. The simulation results proved that the spatial distribution of water demand has positive correlation with population density in urban area. We observed the great difference of water demand intensity among different types land use with great difference in the process of urban water demand. The results also showed the excellent performance of the distributed model for urban water demand prediction both in calibration and validation when applied in Xiamen urban water demand prediction. Compared with the traditional urban water demand prediction model, the distributed water demand prediction model, with bright application prospect, can obtain the distributed urban water demand data with relatively stable parameters which can be determined by water demand data and land use data in the past years.

A 3D dynamic visualization method coupled with an urban drainage model

Experimental study of rainwater grate blocking and submergence of outfall on drainage network capacity