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Water Resources Management

Erschienen in:

03.02.2022

Green Roof Hydrological Modelling With GRU and LSTM Networks

verfasst von: Haowen Xie, Mark Randall, Kwok-wing Chau

Erschienen in: Water Resources Management | Ausgabe 3/2022

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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.

Vorheriger Artikel Evaluation of the Impact of Best Management Practices on Streamflow, Sediment and Nutrient Yield at Field and Watershed Scales

Nächster Artikel Improving River-Stage Forecasting Using Hybrid Models Based on the Combination of Multiple Additive Regression Trees and Runge–Kutta Schemes

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Titel: Green Roof Hydrological Modelling With GRU and LSTM Networks
verfasst von: Haowen Xie
Mark Randall
Kwok-wing Chau
Publikationsdatum: 03.02.2022
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 3/2022
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-022-03076-6

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