A Risk-Based Approach in Rehabilitation of Water Distribution Networks
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Debón, A. & Carrión, A. & Cabrera, E. & Solano, H., 2010. "Comparing risk of failure models in water supply networks using ROC curves," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 43-48.
- Kabir, Golam & Tesfamariam, Solomon & Francisque, Alex & Sadiq, Rehan, 2015. "Evaluating risk of water mains failure using a Bayesian belief network model," European Journal of Operational Research, Elsevier, vol. 240(1), pages 220-234.
- Eric D. Smith & William T. Siefert & David Drain, 2009. "Risk matrix input data biases," Systems Engineering, John Wiley & Sons, vol. 12(4), pages 344-360, December.
- Ahmed M. A. Sattar & B. Gharabaghi & Edward A. McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.
- Ahmed Sattar & B. Gharabaghi & Edward McBean, 2016. "Prediction of Timing of Watermain Failure Using Gene Expression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1635-1651, March.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
- Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
- Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
- Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
- Shaghaghi, Saba & Bonakdari, Hossein & Gholami, Azadeh & Ebtehaj, Isa & Zeinolabedini, Maryam, 2017. "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 271-286.
- Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
- Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model," OSF Preprints kaeny, Center for Open Science.
- Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
- Stephane Helleringer & Chong You & Laurence Fleury & Laetitia Douillot & Insa Diouf & Cheikh Tidiane Ndiaye & Valerie Delaunay & Rene Vidal, 2019. "Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(9), pages 219-260.
- Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
- Thiemo Fetzer & Stephan Kyburz, 2024.
"Cohesive Institutions and Political Violence,"
The Review of Economics and Statistics, MIT Press, vol. 106(1), pages 133-150, January.
- Thiemo Fetzer & Stephan Kyburz, 2018. "Cohesive Institutions and Political Violence," HiCN Working Papers 271, Households in Conflict Network.
- Thiemo Fetzer & Stephan Kyburz, 2019. "Cohesive Institutions and Political Violence," Working Papers 503, Center for Global Development.
- Thiemo Fetzer & Stephan Kyburz, 2018. "Cohesive Institutions and Political Violence," OxCarre Working Papers 210, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
- Fetzer, Thiemo & Kyburz, Stephan, 2018. "Cohesive Institutions and Political Violence," CAGE Online Working Paper Series 377, Competitive Advantage in the Global Economy (CAGE).
- Thiemo Fetzer & Stephan Kyburz, 2018. "Cohesive Institutions and Political Violence," Empirical Studies of Conflict Project (ESOC) Working Papers 11, Empirical Studies of Conflict Project.
- Fetzer, Thiemo & Kyburz, Stephan, 2018. "Cohesive Institutions and Political Violence," The Warwick Economics Research Paper Series (TWERPS) 1166, University of Warwick, Department of Economics.
- Dang, Hai-Anh & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024.
"Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda,"
GLO Discussion Paper Series
1445, Global Labor Organization (GLO).
- Dang, Hai-Anh H & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," IZA Discussion Papers 17064, Institute of Labor Economics (IZA).
- Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
- Sascha O. Becker & Thiemo Fetzer, 2018.
"Has Eastern European Migration Impacted UK-born Workers?,"
CAGE Online Working Paper Series
376, Competitive Advantage in the Global Economy (CAGE).
- Becker, Sascha O. & Fetzer, Thiemo, 2018. "Has Eastern European Migration Impacted UK-born Workers?," The Warwick Economics Research Paper Series (TWERPS) 1165, University of Warwick, Department of Economics.
- Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2019.
"Can media and text analytics provide insights into labour market conditions in China?,"
International Journal of Forecasting, Elsevier, vol. 35(3), pages 1118-1130.
- Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2019. "Can media and text analytics provide insights into labour market conditions in China?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
- Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2018. "Can media and text analytics provide insights into labour market conditions in China?," BOFIT Discussion Papers 9/2018, Bank of Finland Institute for Emerging Economies (BOFIT).
- Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2018. "Can Media and Text Analytics Provide Insights into Labour Market Conditions in China?," Staff Working Papers 18-12, Bank of Canada.
- Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
- Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
- Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
More about this item
Keywords
risk-based rehabilitation; risk analysis; pipe break; Random Forest; reliability;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1594-:d:738813. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.