Flood forecast and flood vulnerability modeling in case of Wadi Fez, Morocco

Fathi, Badr El; Hassani, Farah El; Moukhliss, Mohammed; Mazigh, Nouhaila; Dra, Abdelaziz; Ouallali, Abdessalam; Kherbeche, Abdelhak; Taleb, Abdeslam

doi:10.1007/s12517-022-09760-6

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Similar to Tangier in the northwest of the country, which experienced exceptional floods in 2008 [18], Wed Fes [19], and Ourika [20], "Midar" was also flooded in 2008 as a result of the increased levels of water in "Kart Valley" (as shown in Figure 2). This flood resulted in significant material damages, prompting the authorities at the time to implement a range of measures to prevent similar losses.…”

Section: Study Areamentioning

confidence: 99%

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

Akallouch,

Al Mashoudi,

Ziani

et al. 2024

OJE

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The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar's population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.

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Section: Study Areamentioning

confidence: 99%

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

Akallouch,

Al Mashoudi,

Ziani

et al. 2024

OJE

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“…Under the influence of changing environment, the runoff formation process and evolution mechanism are complex and variable, and the basin steady-state assumption is no longer valid [1][2]. Currently, it is important to explore disaster forecasting models suitable for different basins to achieve proactive and scientific flood control [3].…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Method Based on Improved VMD-FOS-QR-RBL

Liu

Li-hu

et al. 2023

IEEE Access

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To improve the accuracy, reliability and validity of flood prediction models, this study proposes a regularized broad learning (RBL) model based on an improved variational mode decomposition (VMD). Firstly, grey correlation analysis was used to improve the endpoint effect of the VMD and the particle swarm optimisation (PSO) algorithm was used to optimise the VMD parameters. Then, using orthogonal triangular decomposition (QR), redefining the hidden layer output of BL model and adding forgettable online sequence learning mechanism (FOS) to construct online sequence BL (FOS-QR-RBL), which can significantly improve the computational efficiency of BL model. Finally, a flood forecasting method based on improved VMD-FOS-QR-RBL was constructed by combining the FOS-QR-RBL with the improved VMD and applying it to regional flood forecasting. The experimental results show that the computational efficiency of FOS-QR-RBL is improved by 35% and 23.68% compared with RBL and QR-RBL, respectively. The mean absolute error (MAE) of the coupling model of VMD and FOS-QR-RBL is reduced by 80.30% and 84.10% respectively, and the nash efficiency coefficient (Ens) is increased by 15.51% and 28.16% respectively, compared with that of the coupling model of FOS-QR-RBL with ensemble empirical mode decomposition (EEMD) and adaptive noise complete ensemble empirical mode decomposition (CEEMDAN). The results of the optimal operation based on VMD-FOS-OR-RBL show that the model can effectively reduce the economic losses caused by regional flooding.

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