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Method for detecting spatial aggregation of urban water network leakage and identifying key influencing factors

A technology of influencing factors and identification methods, applied in the field of spatial assessment of urban water network leakage problems

Active Publication Date: 2019-01-11
BEIJING JIAOTONG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

Cluster Reliability Analysis of Spatial Statistical Scan Data
Establishment of Leakage Aggregation Prediction Model Using Probabilistic Neural Network

Method used

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  • Method for detecting spatial aggregation of urban water network leakage and identifying key influencing factors
  • Method for detecting spatial aggregation of urban water network leakage and identifying key influencing factors
  • Method for detecting spatial aggregation of urban water network leakage and identifying key influencing factors

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Embodiment Construction

[0076] The following is attached Figure 1-5 The present invention is described in further detail.

[0077] A method for detecting spatial aggregation of urban water network leakage and identifying key influencing factors, comprising the following steps:

[0078] Step 1: Load the reference case data of urban water network leakage

[0079] The study area is the city of Los Angeles. Los Angeles City is located in the south-central part of Los Angeles County, with a population of 9.89 million. The city of Los Angeles can be divided into 1003 census tracts. In more detail, Los Angeles can be divided into 2504 census block groups. Census block groups are statistical divisions of census blocks, and each census block contains at least one census group. Each group is defined to have approximately 600 to 3000 inhabitants. Census block groups are collected, tabulated, and presented by the U.S. Census Bureau. In this specific embodiment, the census block group is selected as the ge...

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Abstract

The invention relates to a method for detecting spatial aggregation of urban water network leakage and identifying key influencing factors. The method comprises the following steps: 1, loading basic data information of urban water network leakage; 2, calculating the spatial aggregation of the urban water network, which comprises the following sub-steps: sub-step 1) determining a scanning mode, that is, how to define a position and a size of a scanning window; sub-step 2) calculating statistics; sub-step 3) carrying out significance analysis; sub-step 4) carrying out cluster analysis; 3, calculating the reliability score of the spatial aggregation of the urban water network; 4, analyzing that evolution of the spatial agglomeration of the urban water network; 5, establishing a prediction model of factors affect that spatial agglomeration of the urban water network based on the probabilistic neural network, which comprises the following sub-steps: sub-step 1) carrying out dependent variable selection; sub-step 2) carrying out factor selection; sub-step 3) carrying out probabilistic neural network modeling; 6, screening key influencing factors.

Description

technical field [0001] The invention relates to a method for spatial aggregation detection of urban water network leakage and identification of key influencing factors. The invention is mainly used in spatial assessment of urban water network leakage problems. Background technique [0002] The urban water supply network is one of the key urban infrastructures, and its stability helps to ensure the quality of life of local residents and the normal operation of institutions and industries. However, the aging of the urban water supply network is causing great distress, resulting in a large amount of water waste, indirect economic losses and a reduction in the quality of life of residents. [0003] In order to solve the problems caused by leakage, it is necessary to analyze from the perspective of the spatial aggregation of water network leakage, and identify the high and low aggregation areas where leakage is found in space. Replace the pipe segment with the auxiliary manager ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
CPCG06N3/02G06Q10/04G06Q50/06Y02A20/00
Inventor 双晴
Owner BEIJING JIAOTONG UNIV
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