
Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown
Published: Feb. 22, 2024
Language: Английский
Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown
Published: Feb. 22, 2024
Language: Английский
Water Research, Journal Year: 2025, Volume and Issue: 274, P. 123091 - 123091
Published: Jan. 6, 2025
Language: Английский
Citations
10Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 95, P. 104601 - 104601
Published: April 26, 2023
Language: Английский
Citations
27Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 96, P. 104625 - 104625
Published: May 6, 2023
Language: Английский
Citations
24Water Resources Management, Journal Year: 2023, Volume and Issue: 37(9), P. 3769 - 3793
Published: May 24, 2023
Abstract Rapid urbanization has increased impervious areas, leading to a higher flood hazard across cities worldwide. Low Impact Development (LID) practices have shown efficacy in reducing urban runoff; nevertheless, choosing the best combinations terms of implementation cost and performance is great importance. The present study introduces framework based on green infrastructure, multi-objective optimization, decision support tools determine most cost-effective LID solutions. Storm Water Management Model (SWMM) was employed for rainfall-runoff hydraulic modeling Region 1, District 11 Tehran, Iran. Six scenarios different were developed. system Urban Stormwater Treatment Analysis Integration (SUSTAIN) used optimize evaluate each scenario. selected solutions imported SWMM stormwater performance. Then, two multi criteria making (MCDM) models, including TOPSIS COPRAS, rank four technical economic criteria. Results showed that scenario 4, consisting rain barrels, porous pavements, vegetated swales, had under with 7.68 million USD reduced runoff volume peak flow by 20.77% 19.2%, respectively. However, Under COPRAS method, Scenario 2 combination bio-retention cells, swales than other 3.25 led 15% reduction 4.30% flow. method more sensitive weights chose economical as ideal. 4 concluded be feasible due spatial limitations area. proposed SWMM—SUSTAIN—MCDM could helpful decision-makers design, evaluation, estimation, selection optimal scenarios.
Language: Английский
Citations
21Water Resources Management, Journal Year: 2024, Volume and Issue: 38(12), P. 4517 - 4540
Published: May 6, 2024
Language: Английский
Citations
7Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130076 - 130076
Published: Aug. 10, 2023
The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With models, the choice a spatio-temporal discretization scheme for computational domain strenuous task that requires extensive calibration potentially lab-based experimentation. GSI has high temporal dynamics due to natural, anthropogenic, climatic processes are not well represented by traditional which calibrated against only few historical observations have user-defined constrained set outcomes. Deep learning-based predictive such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity quantify performance, accounting its highly dynamic constantly evolving nature leveraging advancements in observational data. A LSTM regression can overcome some limitations associated with hydrological aid development fully data-informed predictor. To demonstrate model outcomes, both methods were applied rain garden Villanova, PA, USA. Specifically, was used predict recession ponded water depth using five years observed eight predictors (i.e., precipitation, air temperature, soil moisture content at 10 cm, 35 65 cm 91 depth) target variable rate) considered training/testing model. comparative study USEPA Storm Water Management Model (SWMM) performed observe continuous rate time series specific storms. had score, Root Mean Square Error (RMSE), 0.081 series, outperforming SWMM score 2.173 when compared In case storm-specific prediction, also outperformed simulation four storms lower RMSE values application predicting crucial stride towards efficient real-time forecasting.
Language: Английский
Citations
16Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 478, P. 143921 - 143921
Published: Oct. 9, 2024
Language: Английский
Citations
4Published: Jan. 1, 2025
Language: Английский
Citations
0Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2587 - 2587
Published: March 14, 2025
Climate change and urbanization are increasingly threatening urban environments through pluvial flooding, prompting the widespread use of coupled hydrological–hydrodynamic models. These models provide accurate flood simulations forecasting capabilities, they can analyze benefits low-impact development stormwater control measures in surface-flooding processes. However, most studies have primarily focused on analyzing effects for specific events, lacking an analytical framework that accounts uncertainty. This research proposes a evaluating uncertainty pluvial-flood control, combining urban-scale simulation, modeling, analysis, while constructing nonlinear dependencies between features reflecting surface-flood-control benefits. Based analysis copula methods, this aims to support sustainable planning decision-making approach management. The results show assessment method based generalized likelihood is effective. By comparing posterior joint distribution with prior distribution, different governance performance metrics, joint, synergistic, conditional, combined all exhibit consistent trends as metrics change. current presents innovative simulating at scale, providing valuable insights mitigation strategies.
Language: Английский
Citations
0Water Resources Management, Journal Year: 2025, Volume and Issue: unknown
Published: April 8, 2025
Language: Английский
Citations
0