Green Roof Hydrological Modelling With GRU and LSTM Networks

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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.

Original languageEnglish
JournalWater Resources Management
Issue number3
Pages (from-to)1107–1122
Number of pages16
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.

    Research areas

  • Green Roof, GRU, Hydrologic Modelling, LSTM, Machine Learning

ID: 297387334