Flood–pedestrian simulator for modelling human  response dynamics during flood-induced  evacuation: Hillsborough stadium case study

Shirvani, Mohammad; Kesserwani, Georges

doi:10.5194/nhess-21-3175-2021

Cited by 16 publications

(4 citation statements)

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“… Average Hazard Rate (HR ave ) and Maximum Hazard Rate (HR max ). The flood HR is defined as: HR = h × ( V + 0.5); and, it has been used to provide more information on velocity impacts to assets including structural damage to residential buildings, damages to road infrastructures, and risks to people's life and injury (Kreibich et al., 2009; Lumbroso & Davison, 2018; Maranzoni et al., 2022; Ramsbottom et al., 2006; Shirvani & Kesserwani, 2021; Shirvani et al., 2021). In this study, HR ave and HR max quantify the average and maximum flood HR over the computational area. Floodtiming .…”

Section: Uq Analysis Frameworkmentioning

confidence: 99%

“…This number should be appropriate informed by Beevers et al. (2020) and Shirvani and Kesserwani (2021), where the authors reported making 10 and 20 replications to average acceptable differences with SMC and LHS, respectively.…”

Section: Uq Analysis Frameworkmentioning

confidence: 99%

“…Flood risks are predicted to increase under global warming, affecting people's health, environment, and socio‐economic activities (Arnell & Gosling, 2016; Gu et al., 2022). Flood prediction uses a flood physical solver to estimate two‐dimensional maps of flood‐related quantities of interest, including the Flood Extent and/or the flood Hazard Rating (HR) that can be further used to assess impacts from the velocity magnitude (Kreibich et al., 2009; Nkwunonwo et al., 2020; Shirvani et al., 2021; Shirvani & Kesserwani, 2021). A deterministic flood physical solver does not account for the variability inherent in the input variables, due to measurement error uncertainty, which might lead to suboptimal decisions in flood management and mitigation strategies (Di Baldassarre et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

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Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

Hajihassanpour

Kesserwani

Pettersson

et al. 2023

Water Resources Research

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In probabilistic flood modeling, uncertainty manifests in frequency of occurrence, or histograms, for quantities of interest, including the Flood Extent and hazard rating (HR). Such modeling at the field‐scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a relatively reduced sample size, including detailed histograms of quantities of interest. Latin hypercube sampling (LHS) is the most evaluated alternative for fluvial floods but yields no considerable sample size reduction. Potentially better alternatives include adaptive stratified sampling (ASS), Quasi Monte Carlo (QMC) and Haar‐wavelet expansion (HWE), which are yet unevaluated for probabilistic flood modeling. To fulfill this gap, LHS, ASS, QMC, and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for Flood Extent, and average and maximum HR—while keeping the difference below 10% to the reference SMC prediction. The comparison is done for two test cases with two (i.e., inflow discharge and Manning's coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. Accompanying research codes are openly available online.

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Section: Uq Analysis Frameworkmentioning

confidence: 99%

Section: Uq Analysis Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

Hajihassanpour

Kesserwani

Pettersson

et al. 2023

Water Resources Research

Self Cite

View full text Add to dashboard Cite

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“…Although flood hazard maps are commonly used to estimate dangers, they frequently fail to address the unique vulnerabilities of pedestrians and drivers (Arosio et al, 2021; Kvočka et al, 2016). The complexity of assessing pedestrian safety extends to evaluating individual risks in varying environmental conditions (Xu et al, 2017) and understanding the nuances of pedestrian behavior during floods (Shirvani & Kesserwani, 2021).…”

Section: Introductionmentioning

confidence: 99%

Development of a hazard risk map for assessing pedestrian risk in urban flash floods: A case study in Cúcuta, Colombia

Corzo Perez,

Sanchez Tapiero,

Contreras Martínez

et al. 2024

River

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The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities, leading to severe flooding and significant economic losses for civilians. This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians, which is often overlooked in traditional flood hazard analyses. This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods, with a specific focus on Cúcuta, Colombia. The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling. Advanced computational algorithms for image recognition were used to measure water levels at 5‐s intervals on November 6, 2020, using drones for digital elevation model data collection. In Cúcuta, where flood risk is high and drainage infrastructure is limited, the PCSWMM (Computer‐based Urban Stormwater Management Model) was calibrated and validated to simulate extreme flood events. The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin. Four return periods (5, 10, 50, 100), with extreme rainfall of 3 h, were used to estimate the variability of the risk map. The output of the model was analyzed, and an integrated and time‐varying comparison of the results was done. Results show that the regions of high‐water depth and high velocity could vary significantly along the duration of the different extreme events. Also, from 5 to 100 years return period, the percentage of area at risk increased from 9.6% to 16.6%. The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually. This study identified medium to high‐risk locations, which are dynamic in time. We can conclude dynamics are spatiotemporal, and the added information layer of pedestrians brings vulnerability information that is also dynamic. Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events. The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.

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A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences

Qin,

Liang,

Chen

et al. 2024

Environmental Modelling & Software

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Flood–pedestrian simulator for modelling human response dynamics during flood-induced evacuation: Hillsborough stadium case study

Cited by 16 publications

References 50 publications

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

Development of a hazard risk map for assessing pedestrian risk in urban flash floods: A case study in Cúcuta, Colombia

A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences

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