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Green Roof Hydrological Modelling With GRU and LSTM Networks

Author

Listed:
  • Haowen Xie

    (Hohai University)

  • Mark Randall

    (University of Copenhagen)

  • Kwok-wing Chau

    (Hong Kong Polytechnic University)

Abstract

Green Roofs (GRs) are increasing in popularity due to their ability to manage roof runoff while providing a number of additional ecosystem services. Improvement of hydrological models for the simulation of GRs will aid design of individual roofs as well as city scale planning that relies on the predicted impacts of widespread GR implementation. Machine learning (ML) has exploded in popularity in recent years, however there are no studies focusing on the use of ML in hydrological simulation of GRs. We focus on two types of ML-based model: long short-term memory (LSTM) and gated recurrent unit (GRU), in modelling GRs hydrological performance, with sequence input andsingle output (SISO), and synced sequence input and output (SSIO) architectures. Results of this paper indicate that both LSTM and GRU are useful tools for GR modelling. As the time window length (memory length, time step length of input data) increases, SISO appears to have a higher overall forecast accuracy. SSIO delivers the best overall performance, when the SSIO is close to, or even exceeds, the maximum window size.

Suggested Citation

  • Haowen Xie & Mark Randall & Kwok-wing Chau, 2022. "Green Roof Hydrological Modelling With GRU and LSTM Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1107-1122, February.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:3:d:10.1007_s11269-022-03076-6
    DOI: 10.1007/s11269-022-03076-6
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    References listed on IDEAS

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    1. Berardi, Umberto & GhaffarianHoseini, AmirHosein & GhaffarianHoseini, Ali, 2014. "State-of-the-art analysis of the environmental benefits of green roofs," Applied Energy, Elsevier, vol. 115(C), pages 411-428.
    2. Sangita Dey & Arabin Kumar Dey & Rajesh Kumar Mall, 2021. "Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3395-3410, August.
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    Cited by:

    1. Jiachang Wang & Jun Wang & Shengle Cao & Chuanqi Li & Shouhong Zhang & Yiping Guo, 2024. "A Framework for Quantifying Stormwater Control Measures’ Hydrologic Performance with Analytical Stochastic Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5385-5402, October.
    2. Seyedeh Hadis Moghadam & Parisa-Sadat Ashofteh & Hugo A. Loáiciga, 2022. "Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3181-3205, July.

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