TY - JOUR

T1 - Green Roof Hydrological Modelling With GRU and LSTM Networks

AU - Xie, Haowen

AU - Randall, Mark

AU - Chau, Kwok wing

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Green Roof

KW - GRU

KW - Hydrologic Modelling

KW - LSTM

KW - Machine Learning

U2 - 10.1007/s11269-022-03076-6

DO - 10.1007/s11269-022-03076-6

M3 - Journal article

AN - SCOPUS:85124168382

VL - 36

SP - 1107

EP - 1122

JO - Water Resources Management

JF - Water Resources Management

SN - 0920-4741

IS - 3

ER -