Pipe damage probability prediction method based on BP neural network

Abstract

The invention discloses a pipe damage probability prediction method based on a BP neural network, and belongs to the technical field of urban water supply pipe networks. The method comprises the steps: carrying out the early data preparation for the prediction of the pipe damage probability; selecting a to-be-predicted pipe network region, and determining a training data range through the whole pipe network, an administrative area and a peripheral buffering region; selecting an influence factor causing the damage to a pipe, and extracting a training sample, needed by modeling, in the training data range; training a model through employing a BP neural network algorithm and the training sample, and evaluating the stability and prediction precision of the model through employing an ROC curve based on 10-fold cross validation; applying the trained model in the to-be-predicted pipe network region, and obtaining the probability that each pipe will be damaged; classifying the pipe damage probability in ArcGIS through employing a natural break point method, and making a thematic map. The method enlarges the research content of a conventional pipe damage probability prediction model, and provides a new idea for the building of a water supply pipe network asset management scientific method.

Description

technical field [0001] The invention relates to a method for predicting the probability of pipeline damage, which belongs to the field of urban water supply pipeline networks. Background technique [0002] As the lifeblood of the city to ensure normal production and life, the water supply network is the core asset of the water supply enterprise, accounting for 65-75% of the total assets of the enterprise. The pipe network is buried deep underground, so it is difficult to detect and consume manpower and material resources. The damage caused by various reasons every year makes the asset depreciate very seriously. my country's water supply network construction lags behind the overall urban construction level, lacks scientific asset management methods, and basic data is not yet perfect. These problems have always restricted the management efficiency and service level of water supply companies. Therefore, it is of great economic and practical significance to predict the occurren...

AI Technical Summary

Problems solved by technology

The pipeline network is buried deep underground, which is difficult to detect and consumes manpower and material resources. The damage caused by various reasons every year makes the asset depreciate very seriously

my country's water supply network construction lags behind the overall urban construction level, lacks scientific asset management methods, and basic data is not yet perfect. These problems have always restricted the management efficiency and service level of water supply companies

Most of the existing data analysis models for predicting pipeline damage are based on the basic attribute data of the pipeline itself. Due to the limitation of data integrity, they lack the consideration of surrounding geographical environment factors; most of them evaluate the accuracy of the model, and lack of discussion on the stability of the model

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