Research On The Application Of Data Mining Technology In Leakage Analysis Of Community Water Supply Network

Community water supply network is an important infrastructure for urban residents’ daily life,which plays an important role in ensuring economic development and improving residents’ living quality.However,with the increase of use time,the water supply pipe network in the community will face problems such as aging of pipes and weak bearing capacity,which leads to the continuous occurrence of pipe network leakage.In order to discover the leakage of pipe network in time and reduce the waste of water resources,based on the historical data of the state of water supply pipe network in a community,this dissertation uses data mining technology to analyze the leakage of the pipe network in the community,and effectively helps the staff in the community to discover and deal with the leakage of water supply pipe network in time.Firstly,the leakage factors and leakage water of pipe network are analyzed.Based on the detection data of pipe network leakage in a residential area,this dissertation analyzes the factors affecting the pipe network leakage in detail.At the same time,the method of analyzing the leakage of pipe network is studied.By obtaining the daily water flow data of the residential area,the leakage state of the pipe network in the residential area is analyzed by the method of combining the night minimum flow method and the normal distribution model,and the leakage of the pipe network is obtained.On this basis,the leakage threshold of the pipe network is calculated as the standard of judging the leakage of the water network in the residential area.Secondly,the analysis model of water supply network leakage is established.By obtaining the data of the monitoring points of the residential water supply network,according to the characteristics of the data set and the factors affecting the leakage of the residential network,put forward the improved SVM model based on SMOTE algorithm,SMOTE algorithm,GA optimization algorithm,smote algorithm and PSO optimization algorithm,and analyze the leakage of the monitoring points of the network.Namely,smote-SVM and PSMOTE-SVM optimization model.Meanwhile,the experimental comparison and analysis between the two established optimization models and three classic data mining classification algorithms show that the two proposed optimization models in this dissertation are better than support vector machine,naive Bayes and random forest in leakage analysis of the residential water supply network,and the classification accuracy of GSMOTE-SVM optimization model is the best.The performance of pipe network leakage analysis is more excellent,which provides a scientific basis for the effective analysis of the leakage of pipe network monitoring points.Finally,the water supply network leakage analysis system is developed.According to the actual demand of pipe network leakage analysis,the leakage threshold obtained from the leakage water analysis is applied to the leakage judgment module of the system as the standard of judging pipe network leakage.Apply the established GSMOTE-SVM optimization model in the leakage analysis module of the system to analyze the leakage of the monitoring points of the pipe network.Finally,a management system integrating network data management,leakage judgment and leakage analysis is realized,which is convenient for the staff in the community to analyze the leakage of water supply network in a more timely and reasonable manner.