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Application Research on Risk Assessment of Municipal Pipeline Network Based on Random Forest Machine Learning Algorithm
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Integration of feature vector selection and support vector machine for classification of imbalanced data
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Risk-based asset management of water piping networks using neurofuzzy systems
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Novel leakage detection and water loss management of urban water supply network using multiscale neural networks
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Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation
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A selective literature review of transient-based leak detection methods
inferredcites →https://doi.org/10.1016/j.mcm.2009.12.033openalex-snapshot
Application of Artificial Neural Networks (ANN) to model the failure of urban water mains
inferredcites →https://doi.org/10.1016/j.patcog.2015.03.009openalex-snapshot
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
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Random forest regression prediction of solid particle Erosion in elbows
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Statistical models for the analysis of water distribution system pipe break data
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Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system
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Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier
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Real time control of water distribution networks: A state-of-the-art review
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Deep learning identifies accurate burst locations in water distribution networks
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Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system
inferredcites →https://doi.org/10.1061/(asce)0733-9429(1992)118:7(1031)openalex-snapshot
Leaks in Pipe Networks
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Hierarchical Fuzzy Expert System for Risk of Failure of Water Mains
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Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows
inferredcites →https://doi.org/10.1080/1573062x.2017.1279191openalex-snapshot
A review of data-driven approaches for burst detection in water distribution systems
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Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems
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Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey
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Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
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SMOTE: Synthetic Minority Over-sampling Technique
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Remote real-time pressure control via a variable speed pump in a specific water distribution system
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Geostatistical techniques for approximate location of pipe burst events in water distribution systems
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Heuristic burst detection method using flow and pressure measurements
inferredcites →https://doi.org/10.2166/ws.2014.131openalex-snapshot
A review of methods for burst/leakage detection and location in water distribution systems
inferredcites →https://doi.org/10.2166/wst.2002.0595openalex-snapshot
A neural network approach to burst detection
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Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam
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The Application of Case Teaching Method for “Python and Application” under the Concept of Curriculum Ideology and Politics
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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Finite Element Analysis-Unascertained Measure Theory-Based Hybrid Approach to Safety Assessment for Pipelines Subject to Landslide Disasters
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