Effect of climate change on the flooding of storm water networks under extreme rainfall events using SWMM simulations: a case study

Hassan, Waqed H.; Nile, Basim K.; Kadhim, Zahraa K.

doi:10.1007/s40808-024-01998-1

Model. Earth Syst. Environ.

2024

DOI: 10.1007/s40808-024-01998-1

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Effect of climate change on the flooding of storm water networks under extreme rainfall events using SWMM simulations: a case study

Waqed H. Hassan,

Basim K. Nile,

Zahraa K. Kadhim

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2024

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Cited by 2 publications

References 95 publications

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Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

Frisk,

Johansson

2024

Water

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This study evaluates the accuracy of water level forecasting using two approaches: the hydrodynamic model SWMM and machine learning (ML) models based on the Nonlinear Autoregressive with Exogenous Inputs (NARX) framework. SWMM offers a physically based modeling approach, while NARX is a data-driven method. Both models use real-time precipitation data, with their predictions compared against measurements from a network of IoT sensors in a stormwater management system. The results demonstrate that while both models provide effective forecasts, NARX models exhibit higher accuracy, with improved Nash–Sutcliffe Efficiency (NSE) coefficients and 33–37% lower mean absolute error (MAE) compared to SWMM. Despite these advantages, NARX models may struggle with limited data on extreme flooding events, where they could face accuracy challenges. Enhancements in SWMM modeling and calibration could reduce the performance gap, but the development of SWMM models requires substantial expertise and resources. In contrast, NARX models are generally more resource-efficient. Future research should focus on integrating both approaches by leveraging SWMM simulations to generate synthetic data, particularly for extreme weather events, to enhance the robustness of NARX and other ML models in real-world flood prediction scenarios.

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Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

Frisk,

Johansson

2024

Water

View full text Add to dashboard Cite

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Review of Green Water Systems for Urban Flood Resilience: Literature and Codes

Valencia-Félix,

Anco-Valdivia,

Espinoza Vigil

et al. 2024

Water

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Achieving Urban Flood Resilience (UFR) is essential for modern societies, requiring the implementation of effective practices in different countries to mitigate hydrological events. Green Water Systems (GWSs) emerge as a promising alternative to achieve UFR, but they are still poorly explored and present varied definitions. This article aims to define GWSs within the framework of sustainable practices and propose a regulation that promotes UFR. Through a systematic review of existing definitions and an analysis of international regulations on sustainable urban drainage systems (SuDSs), this study uncovers the varied perceptions and applications of GWSs and their role in Blue–Green Infrastructure (BGI). Furthermore, the research puts forth a standardized definition of GWSs and emphasizes the implementation of SuDSs in Peru. This approach aims to address the existing knowledge gap and contribute to the advancement of sustainable urban infrastructure.

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Effect of climate change on the flooding of storm water networks under extreme rainfall events using SWMM simulations: a case study

Cited by 2 publications

References 95 publications

Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

Comparative Evaluation of Water Level Forecasting Using IoT Sensor Data: Hydrodynamic Model SWMM vs. Machine Learning Models Based on NARX Framework

Review of Green Water Systems for Urban Flood Resilience: Literature and Codes

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