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

Author

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  • 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
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    References listed on IDEAS

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