Evaluation of data driven models for pipe burst prediction in urban water distribution systems-Reference-Cited by-同舟云学术

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Evaluation of data driven models for pipe burst prediction in urban water distribution systems

Published:2019-02-07 Issue:2 Volume:16 Page:136-145
ISSN:1573-062X
Container-title:Urban Water Journal
language:en
Short-container-title:Urban Water Journal

Author:

Alizadeh Z.¹,Yazdi J.¹,Mohammadiun S.²,Hewage K.³,Sadiq R.³

Affiliation:

1. Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

2. PhD Candidate, University of British Columbia, School of Engineering, Kelowna, Canada

3. School of Engineering, University of British Columbia, Kelowna, Canada

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/1573062X.2019.1637004

Reference33 articles.

1. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

2. Assessment of Machine Learning Techniques for Monthly Flow Prediction

3. Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods

4. Development of pipe deterioration models for water distribution systems using EPR

5. An R-squared measure of goodness of fit for some common nonlinear regression models

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1. Pressure Interpolation in Water Distribution Networks by Using Gaussian Processes: Application to Leak Diagnosis;Processes;2024-06-01

2. Data Driven System Identification of Water Distribution Systems via Kernel-Based Interpolation;World Environmental and Water Resources Congress 2024;2024-05-16

3. Data-driven predictive control strategies of water distribution systems using sparse regression;Journal of Water Process Engineering;2024-03

4. Application of data prediction models in a real water supply network: comparison between arima and artificial neural networks;RBRH;2024

5. Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review;Applied Water Science;2023-10-13

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