Utilizing stacked integration algorithm for risk assessment of urban underground drainage networks
In response to a recent surge in drainage network accidents,there is a pressing need for an objective and efficient risk assessment method.To address this,a new method for assessing the risk of urban underground drainage networks,based on stacked models,has been proposed.Stacked ensemble models,as heterogeneous ensemble algorithms,use the results of three primary learners from the first layer as input variables for training meta-learners of the second layer.This approach helps mitigate disruptions in results caused by data deficiencies in independent machine learning models and significantly enhances modeling accuracy.Fourteen feature variables,including pipeline length,gradient,burial depth,and sedimentation,representing various aspects of pipe networks such as the network's attributes,surrounding environment,and structural defects,are selected to build models aimed at exploring the influencing factors on urban underground drainage network risks.The performance of the stacked ensemble model versus independent machine learning models is evaluated using determination coefficient R-squared,root mean square error,and standard deviation.Additionally,the performance between stacked ensemble algorithms and independent machine learning algorithms is assessed by comparing the operating characteristic curves and areas under the curves of the subjects.The research findings reveal that:(1)Within the study area,the proportion of drainage pipes categorized as Level 4 risk and Level 3 risk is 21.23％and 21.38％respectively,indicating a relatively high proportion of high-risk pipes among the total pipes.This underscores the need for prompt intervention to eliminate pipeline risk factors.(2)The predictive accuracy of the stacked ensemble algorithm is 93.7％,which is significantly higher than that of random forests(91％),decision trees(89％),and support vector machines(78％).This demonstrates the superior evaluation performance of the stacked ensemble algorithm in drainage network risk assessment,compared with independent machine learning algorithms.(3)Besides conventional attributes such as pipeline length and diameter,sedimentation,obstacles,and other factors also play crucial roles in drainage network risk.
万方数据
维普
