Research on leakage prediction of water supply pipe network based on model fusion
The leakage problem of water supply pipe network poses a threat of serious waste of water resources and water pol-lution.By using the pipe network GIS database of an enterprise in central China to build a dataset,the learning curve and reliability curve are analyzed in the traditional classification model,and the random forest(RF),logistic regression(LR)and BP neural net-work(BP)models are screened out and optimized.Finally,a fusion model based on Stacking(Stacking)and Voting(Voting)is in-troduced to overcome the shortcomings of a single prediction model.In the fusion model,these three models are used as the base model and LR as the meta-model.By expanding the width of the model,the performance of the model was successfully improved.The experimental results show that the fusion model predicts water leakage in the water supply network with an accuracy of 94.9%and an AUC value of 0.970 in the test set.The prediction ability of the fusion model is significantly better than any classifier con-structed using only a single feature with good generalization ability and robustness.
water supply networkfusion modelrandom forestBP neural network
NETL
NSTL
万方数据
