Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research

With the acceleration of urbanization in China,the discharge of domestic sewage and industrial wastewater is increasing,and accidents of sewage spilling out and polluting the environment occur from time to time.Problems such as imperfect facilities and backward control methods are com-mon in the urban drainage network systems in China.Efficient drainage not only strengthens infrastructure such as rain and sewage diversion,pollution source monitoring,transportation,drainage and storage but also urgently needs technical means to monitor and optimize production and operation.Aiming at the optimal control of single-stage pumping stations and the coordinated control between two-stage pumping stations,this paper studies the modelling and optimal control of drainage network systems.Based on the Long Short Term Memory(LSTM)water level prediction model of the sewage pumping stations,and then based on the mechanism analysis of drainage pipe network,the factors that may cause the water level change of pumping station are obtained.Grey correlation analysis is carried out on these influencing factors,and the prediction model is established by taking the factors with a high correlation degree as input.The research results show that compared with the traditional prediction model,the LSTM model not only has higher prediction accuracy but also has better inflection point tracking ability.

A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation

Similarity measurement has been a prevailing research topic geographic information science.Geometric similarity measurement inin scaling transformation(GSM_ST)is critical to ensure spatial data quality while balancing detailed information with distinctive features.However,GSM_ST is an uncertain problem due to subjective spatial cognition,global and local concerns,and geometric complexity.Traditional rule-based methods considering multiple consistent conditions require subjective adjustments to characteristics and weights,leading to poor robustness in addressing GSM_ST.This study proposes an unsupervised representation learning framework for automated GSM_ST,using a Graph Autoencoder Network(GAE)and drainage networks as an example.The framework involves constructing a drainage graph,designing the GAE architecture for GSM_ST,and using Cosine similarity to measure similarity based on the GAE-derived drainage embeddings in different scales.We perform extensive experiments and compare methods across 71 drainage networks duringfive scaling transformations.The results show that the proposed GAE method outperforms other methods with a satisfaction ratio of around 88%and has strong robustness.Moreover,our proposed method also can be applied to other scenarios,such as measuring similarity between geographical entities at different times and data from different datasets.

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.

A New Algorithm to Automatically Extract the Drainage Networks and Catchments Based on Triangulation Irregular Network Digital Elevation Model

A new algorithm to automatically extract drainage networks and catchments based on triangulation irregular networks(TINs) digital elevation model(DEM) was developed. The flow direction in this approach is determined by computing the spatial gradient of triangle and triangle edges. Outflow edge was defined by comparing the contribution area that is separated by the steepest descent of the triangle. Local channels were then tracked to build drainage networks. Both triangle edges and facets were considered to construct flow path. The algorithm has been tested in the site for Hawaiian Island of Kaho'olawe, and the results were compared with those calculated by ARCGIS as well as terrain map. The reported algorithm has been proved to be a reliable approach with high efficiency to generate well-connected and coherent drainage networks.

Drainage evolution in intermontane basins at the Qinling-Daba Mountains

River capture is of great significance to landform evolution and hominine migration.In the Qinling-Daba Mountains,there is a viewpoint that Jialing River captured Hanjiang River,but this is still controversial.In this paper,we discuss the drainage evolution processes in intermountain basins at the Qinling-Daba Mountains based on a combination of detrital zircon UPb geochronology and geomorphic indexes.We suggest that the Hanjiang River gradually captured the Jialing River from east to west,accompanied by the evolution of the ancient Yangtze River.In terms of geomorphic evidences,wide valleys did not match with discharge,and a series of wind gaps developed in the Shiquan-Ankang basin.In addition,the valley shapes and width-toheight ratios(Vf)indicate two possible rapid incisions.The hypsometric integrals(HI)reflect that the landform gradually changes from the old stage to the youth stage from west to east.Theχvalues show that the drainage divide is moving to the side of the Yuehe River,and the Yuehe River is gradually shrinking.According to the sedimentary records,the zircon U-Pb age distributions indicate the provenance change.The high-altitude terraces show three age peaks(200–250,400–505,and 700–900 Ma),with the dominant Indosinian age peak(200–250 Ma),while the modern fluvial sediments only show a single peak of Jinning(700–900 Ma).These data show that there are two major river captures:(1)The ancient Hanjiang River cut through the regional compression ridge,and then captured the Hanzhong Basin river system(a part of the ancient Jialing river system)from east to west,and(2)The southern tributary captured the trunk with the uplift of the divide in the Shiquan-Ankang Basin,forming the modern drainage pattern in the upper Hanjiang River.The activities of the regional strike-slip fault,and the associated compression uplift played a key role in the river captures,the drainage evolution,and related landforms in the Shiquan-Ankang basin.In addition,it is shown that the evolution of the upper tributary basins lagged behind the response of the trunk channel to the tectonic activities and river captures.The interconnected wide valleys caused by river capture may have provided convenient geomorphological conditions for human migration into the Qinling-Daba Mountains along those river valleys.

Efficient Priority-Flood depression filling in raster digital elevation models

Depressions in raster digital elevation models(DEM)present a challenge for extracting hydrological networks.They are commonly filled before subsequent algorithms are further applied.Among existing algorithms for filling depressions,the Priority-Flood algorithm runs the fastest.In this study,we propose an improved variant over the fastest existing sequential variant of the Priority-Flood algorithm for filling depressions in floating-point DEMs.The proposed variant introduces a series of improvements and greatly reduces the number of cells that need to be processed by the priority queue(PQ),the key data structure used in the algorithm.The proposed variant is evaluated based on statistics from 30 experiments.On average,our proposed variant reduces the number of cells processed by the PQ by around 70%.The speed-up ratios of our proposed variant over the existing fastest variant of the Priority-Flood algorithm range from 31%to 52%,with an average of 45%.The proposed variant can be used to fill depressions in large DEMs in much less time and in the parallel implementation of the Priority-Flood algorithm to further reduce the running time for processing huge DEMs that cannot be dealt with easily on single computers.