| Autor: |
Kwok Wing Chau, Haowen Xie, Randall Mark |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Xie, H, Randall, M & Chau, K W 2022, ' Green Roof Hydrological Modelling With GRU and LSTM Networks ', Water Resources Management, vol. 36, no. 3, pp. 1107–1122 . https://doi.org/10.1007/s11269-022-03076-6 |
| DOI: |
10.21203/rs.3.rs-922451/v1 |
| Popis: |
Green Roofs (GRs) are becoming more popular as a low-impact building option. They have the potential to minimize peak stormwater runoff while also increasing the quality of runoff from buildings. 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, owing to considerable increases in processing power and data availability. 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 hydrological performance of GRs, with sequence input and a single output (SISO), and synced sequence input and output (SSIO) architectures. According to the results of this paper, LSTM and GRU are useful tools for the modelling of GRs. 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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