Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2021
Research article |
| 15 Nov 2021
| 15 Nov 2021
Evaluating different machine learning methods to simulate runoff from extensive green roofs
Elhadi Mohsen Hassan Abdalla
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway
Vincent Pons
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway
Virginia Stovin
Department of Civil and Structural Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
Simon De-Ville
Department of Civil and Structural Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
Elizabeth Fassman-Beck
Southern California Coastal Water Research Project, Costa Mesa, CA 92626, USA
Knut Alfredsen
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway
Tone Merete Muthanna
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, 7031, Norway
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Cited
20 citations as recorded by crossref.
- Influence of climatic parameters on the probabilistic design of green roofs A. Raimondi et al. 10.1016/j.scitotenv.2022.161291
- Estimation of rainfall–runoff using SCS-CN method and GIS techniques in drought-prone area of Upper Kangsabati Watershed, India A. Saha et al. 10.1007/s40899-022-00731-z
- Runoff from an extensive green roof during extreme events: Insights from 15 years of observations K. Paus & B. Braskerud 10.1002/hyp.15220
- A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia E. Habtemariam et al. 10.3390/en16052317
- aiWATERS: an artificial intelligence framework for the water sector D. Vekaria & S. Sinha 10.1007/s43503-024-00025-7
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique S. Swain et al. 10.1038/s41598-024-53410-8
- A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin M. Abdallah et al. 10.5194/hess-28-1147-2024
- Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
- Estimation of possible locations for green roofs and bioswales and analysis of the effect of their implementation on stormwater runoff control T. Kinoshita & T. Ozaki 10.3389/fclim.2024.1287386
- Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network M. Al Mehedi et al. 10.1016/j.jhydrol.2023.130076
- Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches Y. Wang et al. 10.2166/wst.2024.115
- Forecasting green roofs’ potential in improving building thermal performance and mitigating urban heat island in the Mediterranean area: An artificial intelligence-based approach D. Mazzeo et al. 10.1016/j.applthermaleng.2022.119879
- Understanding the hydrological performance of green and grey roofs during winter in cold climate regions N. Maurin et al. 10.1016/j.scitotenv.2024.174132
- On the use of multi-objective optimization for multi-site calibration of extensive green roofs E. Abdalla et al. 10.1016/j.jenvman.2022.116716
- Modeling the hydrological benefits of green roof systems: applications and future needs Z. Dong et al. 10.1039/D3EW00149K
- Towards improving the calibration practice of conceptual hydrological models of extensive green roofs E. Mohsen Hassan Abdalla et al. 10.1016/j.jhydrol.2022.127548
- Modelling urban stormwater management changes using SWMM and convection-permitting climate simulations in cold areas O. Tamm et al. 10.1016/j.jhydrol.2023.129656
- Unit Operation and Process Modeling with Physics-Informed Machine Learning H. Li et al. 10.1061/JOEEDU.EEENG-7467
- Forecasting Green Roofs’ Potential in Improving Building Thermal Performance and Mitigating Urban Heat Island in the Mediterranean Area: An Artificial Intelligence-Based Approach D. Mazzeo et al. 10.2139/ssrn.4155132
- The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology K. Fang et al. 10.1029/2021WR029583
19 citations as recorded by crossref.
- Influence of climatic parameters on the probabilistic design of green roofs A. Raimondi et al. 10.1016/j.scitotenv.2022.161291
- Estimation of rainfall–runoff using SCS-CN method and GIS techniques in drought-prone area of Upper Kangsabati Watershed, India A. Saha et al. 10.1007/s40899-022-00731-z
- Runoff from an extensive green roof during extreme events: Insights from 15 years of observations K. Paus & B. Braskerud 10.1002/hyp.15220
- A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia E. Habtemariam et al. 10.3390/en16052317
- aiWATERS: an artificial intelligence framework for the water sector D. Vekaria & S. Sinha 10.1007/s43503-024-00025-7
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique S. Swain et al. 10.1038/s41598-024-53410-8
- A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin M. Abdallah et al. 10.5194/hess-28-1147-2024
- Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin H. Tongal & M. Booij 10.1007/s00477-022-02276-1
- Estimation of possible locations for green roofs and bioswales and analysis of the effect of their implementation on stormwater runoff control T. Kinoshita & T. Ozaki 10.3389/fclim.2024.1287386
- Predicting the performance of green stormwater infrastructure using multivariate long short-term memory (LSTM) neural network M. Al Mehedi et al. 10.1016/j.jhydrol.2023.130076
- Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches Y. Wang et al. 10.2166/wst.2024.115
- Forecasting green roofs’ potential in improving building thermal performance and mitigating urban heat island in the Mediterranean area: An artificial intelligence-based approach D. Mazzeo et al. 10.1016/j.applthermaleng.2022.119879
- Understanding the hydrological performance of green and grey roofs during winter in cold climate regions N. Maurin et al. 10.1016/j.scitotenv.2024.174132
- On the use of multi-objective optimization for multi-site calibration of extensive green roofs E. Abdalla et al. 10.1016/j.jenvman.2022.116716
- Modeling the hydrological benefits of green roof systems: applications and future needs Z. Dong et al. 10.1039/D3EW00149K
- Towards improving the calibration practice of conceptual hydrological models of extensive green roofs E. Mohsen Hassan Abdalla et al. 10.1016/j.jhydrol.2022.127548
- Modelling urban stormwater management changes using SWMM and convection-permitting climate simulations in cold areas O. Tamm et al. 10.1016/j.jhydrol.2023.129656
- Unit Operation and Process Modeling with Physics-Informed Machine Learning H. Li et al. 10.1061/JOEEDU.EEENG-7467
- Forecasting Green Roofs’ Potential in Improving Building Thermal Performance and Mitigating Urban Heat Island in the Mediterranean Area: An Artificial Intelligence-Based Approach D. Mazzeo et al. 10.2139/ssrn.4155132
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.
This study investigated the potential of using machine learning algorithms as hydrological...
