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.
Highlights
Traditional stormwater infrastructure promotes rapid runoff from impervious surfaces to receiving water bodies
We focus on two types of Machine learning (ML)-based model: long short-term memory (LSTM) and gated recurrent unit (GRU) in modelling hydrological performance of Green roofs (GRs), with sequence input and a single output (SISO), and synced sequence input and output (SSIO) architectures
For the information entropy, the methods contain more information with the increase of window size, and SSIO has the most information over all events
Summary
Traditional stormwater infrastructure promotes rapid runoff from impervious surfaces to receiving water bodies. GRs provide a number of ecosystem services, e.g. biodiversity support, extension of hard-roof life time, reduction in stormwater runoff and noise, improved building insulation, lowering of air temperatures, the final effect of implementation depends on largely on local context and GR design (Berardi et al , 2014, Getter and Rowe, 2006, Kolokotsa et al , 2013, Lepp, 2008, Oberndorfer et al , 2007). In this regard, GRs hydrological simulation employs a variety of models. Curve methods (CM), linear/non-linear storage reservoirs (LSR), single reservoir models (SR), and physical models (PM) are four methods accessible for modelling a green roof in general (Daniel Roehr, 2010, Emmanuel Berthier, 2011, Hilten et al , 2008, Li and Babcock, 2015, Rasmussen, 2006, Soulis et al , 2017, Versini et al , 2015, Xie and Liu, 2020, Xie et al , 2020)
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