IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i12p4600-d188045.html
   My bibliography  Save this article

Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Author

Listed:
  • Junyu Zhang

    (Joint Research Centre for Water Sensitive Cities, Southeast University-Monash University Joint Graduate School (Suzhou), Southeast University, Suzhou 215123, China
    Department of Civil Engineering, Southeast University, #2Sipailou, Nanjing 210096, China)

  • Dafang Fu

    (Joint Research Centre for Water Sensitive Cities, Southeast University-Monash University Joint Graduate School (Suzhou), Southeast University, Suzhou 215123, China
    Department of Civil Engineering, Southeast University, #2Sipailou, Nanjing 210096, China)

  • Christian Urich

    (Joint Research Centre for Water Sensitive Cities, Southeast University-Monash University Joint Graduate School (Suzhou), Southeast University, Suzhou 215123, China
    Department of Civil Engineering, Monash University, Clayton, VIC 3800, Australia)

  • Rajendra Prasad Singh

    (Joint Research Centre for Water Sensitive Cities, Southeast University-Monash University Joint Graduate School (Suzhou), Southeast University, Suzhou 215123, China
    Department of Civil Engineering, Southeast University, #2Sipailou, Nanjing 210096, China)

Abstract

In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman’s Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making.

Suggested Citation

  • Junyu Zhang & Dafang Fu & Christian Urich & Rajendra Prasad Singh, 2018. "Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4600-:d:188045
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/12/4600/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/12/4600/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marjolijn Haasnoot & Hans Middelkoop & Astrid Offermans & Eelco Beek & Willem Deursen, 2012. "Exploring pathways for sustainable water management in river deltas in a changing environment," Climatic Change, Springer, vol. 115(3), pages 795-819, December.
    2. Robert J. Lempert & David G. Groves & Steven W. Popper & Steve C. Bankes, 2006. "A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios," Management Science, INFORMS, vol. 52(4), pages 514-528, April.
    3. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    4. Fan, Xinghua & Wang, Li & Li, Shasha, 2016. "Predicting chaotic coal prices using a multi-layer perceptron network model," Resources Policy, Elsevier, vol. 50(C), pages 86-92.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. Walker, Warren E. & Rahman, S. Adnan & Cave, Jonathan, 2001. "Adaptive policies, policy analysis, and policy-making," European Journal of Operational Research, Elsevier, vol. 128(2), pages 282-289, January.
    7. Thorsten Wiechmann & Karina M. Pallagst, 2012. "Urban shrinkage in Germany and the USA: A Comparison of Transformation Patterns and Local Strategies," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 36(2), pages 261-280, March.
    Full references (including those not matched with items on IDEAS)

    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.
    1. Luciano Raso & Jan Kwakkel & Jos Timmermans, 2019. "Assessing the Capacity of Adaptive Policy Pathways to Adapt on Time by Mapping Trigger Values to Their Outcomes," Sustainability, MDPI, vol. 11(6), pages 1-16, March.
    2. Dittrich, Ruth & Wreford, Anita & Moran, Dominic, 2016. "A survey of decision-making approaches for climate change adaptation: Are robust methods the way forward?," Ecological Economics, Elsevier, vol. 122(C), pages 79-89.
    3. Luciano Raso & Jan Kwakkel & Jos Timmermans & Geremy Panthou, 2019. "How to evaluate a monitoring system for adaptive policies: criteria for signposts selection and their model-based evaluation," Climatic Change, Springer, vol. 153(1), pages 267-283, March.
    4. Sandrine Mathy & Patrick Criqui & Katharina Knoop & Manfred Fischedick & Sascha Samadi, 2016. "Uncertainty management and the dynamic adjustment of deep decarbonization pathways," Climate Policy, Taylor & Francis Journals, vol. 16(sup1), pages 47-62, June.
    5. Lempert Robert J., 2014. "Embedding (some) benefit-cost concepts into decision support processes with deep uncertainty," Journal of Benefit-Cost Analysis, De Gruyter, vol. 5(3), pages 487-514, December.
    6. Erik Pruyt & Jan H. Kwakkel, 2014. "Radicalization under deep uncertainty: a multi-model exploration of activism, extremism, and terrorism," System Dynamics Review, System Dynamics Society, vol. 30(1-2), pages 1-28, January.
    7. Hamarat, Caner & Kwakkel, Jan H. & Pruyt, Erik, 2013. "Adaptive Robust Design under deep uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 408-418.
    8. Ram, Camelia, 2020. "Scenario presentation and scenario generation in multi-criteria assessments: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    9. Hidayatno, Akhmad & Jafino, Bramka Arga & Setiawan, Andri D. & Purwanto, Widodo Wahyu, 2020. "When and why does transition fail? A model-based identification of adoption barriers and policy vulnerabilities for transition to natural gas vehicles," Energy Policy, Elsevier, vol. 138(C).
    10. Fluixá-Sanmartín, Javier & Escuder-Bueno, Ignacio & Morales-Torres, Adrián & Castillo-Rodríguez, Jesica Tamara, 2020. "Comprehensive decision-making approach for managing time dependent dam risks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    11. Pieter Bloemen & Tim Reeder & Chris Zevenbergen & Jeroen Rijke & Ashley Kingsborough, 2018. "Lessons learned from applying adaptation pathways in flood risk management and challenges for the further development of this approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(7), pages 1083-1108, October.
    12. Julie Shortridge & Seth Guikema & Ben Zaitchik, 2017. "Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change," Climatic Change, Springer, vol. 140(2), pages 323-337, January.
    13. Judy Lawrence & Robert Bell & Adolf Stroombergen, 2019. "A Hybrid Process to Address Uncertainty and Changing Climate Risk in Coastal Areas Using Dynamic Adaptive Pathways Planning, Multi-Criteria Decision Analysis & Real Options Analysis: A New Zealand App," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    14. Jan H. Kwakkel, 2019. "A generalized many‐objective optimization approach for scenario discovery," Futures & Foresight Science, John Wiley & Sons, vol. 1(2), June.
    15. Jan Kwakkel & Marjolijn Haasnoot & Warren Walker, 2015. "Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world," Climatic Change, Springer, vol. 132(3), pages 373-386, October.
    16. Mohanasundar Radhakrishnan & Hong Quan Nguyen & Berry Gersonius & Assela Pathirana & Ky Quang Vinh & Richard M. Ashley & Chris Zevenbergen, 2018. "Coping capacities for improving adaptation pathways for flood protection in Can Tho, Vietnam," Climatic Change, Springer, vol. 149(1), pages 29-41, July.
    17. Viera, Oscar & Malekpour, Shirin, 2020. "An analysis of adaptive planning capacity: The case of chilean water utilities," Utilities Policy, Elsevier, vol. 65(C).
    18. Baustert, Paul & Othoniel, Benoit & Rugani, Benedetto & Leopold, Ulrich, 2018. "Uncertainty analysis in integrated environmental models for ecosystem service assessments: Frameworks, challenges and gaps," Ecosystem Services, Elsevier, vol. 33(PB), pages 110-123.
    19. Moallemi, Enayat A. & Elsawah, Sondoss & Ryan, Michael J., 2020. "Robust decision making and Epoch–Era analysis: A comparison of two robustness frameworks for decision-making under uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    20. Julie Shortridge & Janey Smith Camp, 2019. "Addressing Climate Change as an Emerging Risk to Infrastructure Systems," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 959-967, May.

    Corrections

    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:jsusta:v:10:y:2018:i:12:p:4600-:d:188045. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.