Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review

Le, Duan; Liu, Chao; Xu, Hui; Pan, Huali; Liu, Haizhi; Yan, Xufeng; Liu, Tiegang; Yang, Zhengli; Liu, Gang; Dai, Xiaoai; Zhang, Donghui; Xiao, Fu; Liu, Xuemei; Lü, Heng

doi:10.3390/rs14215432

Cited by 6 publications

(3 citation statements)

References 148 publications

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“…Different susceptibility zones were often divided in a single watershed, but the integrity of disaster occurrence was ignored, which is inconsistent with the actual environment and not conducive to accurately identifying flash flood disasters (Sun et al, 2019;Duan et al, 2022).…”

Section: Uncertainty Of Evaluation Unitsmentioning

confidence: 99%

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Yang,

Lu,

Song

et al. 2024

Preprint

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Abstract. Flash flood disasters are one of the major natural disasters in the world, and rapid and accurate early identification of flash flood disasters is the key to preventing and controlling them. In recent years, computer and spatial information technology development has promoted the advancement of early identification technology for flash floods. However, previous research has mainly focused on the impact of "water" and neglected the impact of "sediment" deposition on the rise of water levels. To gain a more comprehensive understanding of flash floods and improve the accuracy of early identification, this article first uses bibliometric methods to review the spatiotemporal distribution, internal relationships, and research hotspots of literature in this field over the past 42 years. Then, the research practice of considering the impact of sediment on the early identification of flash floods was introduced from three aspects: mechanism, model, and uncertainty. Finally, the existing problems in current research were discussed, and future research directions were proposed. The research results have shown that the number of publications in this field has been increasing yearly and will continue to increase, but the cooperation between authors is not close. The coupling effect between sediment replenishment and floods cannot be ignored. Taking into account multiple uncertainties can greatly improve recognition accuracy. This study can provide a panoramic literature perspective and practical research experience for relevant researchers and decision-makers and support further improving flash flood prevention and control capabilities.

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Section: Uncertainty Of Evaluation Unitsmentioning

confidence: 99%

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Yang,

Lu,

Song

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Using open-source data, researchers have used RS and GIS to study natural disasters. According to Duan et al (2022) studies of flood susceptibility assessments experienced an upward trend beginning in 2007. Many of the same variables used in this study were included in these models, such as land use, precipitation, and slope.…”

Section: Flooding Indicesmentioning

confidence: 99%

NeuralFlood: an AI-driven flood susceptibility index

Lin,

Sreng,

Oare

et al. 2023

Front. Water

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Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices' models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.

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“…Statistics show an increase in the number of floods and annual losses from them, models forecast an increase in these trends [1][2][3], and 35-45 percent of the total number of natural disasters are due to hydrological events. The Center for Research on the Epidemiology of Disasters (CRED) 2022 reports highlight floods as the greatest hazard for many regions [4].…”

Section: Introductionmentioning

confidence: 99%

The Problem of Effective Evacuation of the Population from Floodplains under Threat of Flooding: Algorithmic and Software Support with Shortage of Resources

et al. 2023

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Extreme flooding of the floodplains of large lowland rivers poses a danger to the population due to the vastness of the flooded areas. This requires the organization of safe evacuation in conditions of a shortage of temporary and transport resources due to significant differences in the moments of flooding of different spatial parts. We consider the case of a shortage of evacuation vehicles, in which the safe evacuation of the entire population to permanent evacuation points is impossible. Therefore, the evacuation is divided into two stages with the organization of temporary evacuation points on evacuation routes. Our goal is to develop a method for analyzing the minimum resource requirement for the safe evacuation of the population of floodplain territories based on a mathematical model of flood dynamics and minimizing the number of vehicles on a set of safe evacuation schedules. The core of the approach is a numerical hydrodynamic model in shallow water approximation. Modeling the hydrological regime of a real water body requires a multi-layer geoinformation model of the territory with layers of relief, channel structure, and social infrastructure. High-performance computing is performed on GPUs using CUDA. The optimization problem is a variant of the resource investment problem of scheduling theory with deadlines for completing work and is solved on the basis of a heuristic algorithm. We use the results of numerical simulation of floods for the Northern part of the Volga-Akhtuba floodplain to plot the dependence of the minimum number of vehicles that ensure the safe evacuation of the population. The minimum transport resources depend on the water discharge in the Volga river, the start of the evacuation, and the localization of temporary evacuation points. The developed algorithm constructs a set of safe evacuation schedules for the minimum allowable number of vehicles in various flood scenarios. The population evacuation schedules constructed for the Volga-Akhtuba floodplain can be used in practice for various vast river valleys.

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Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review

Cited by 6 publications

References 148 publications

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

NeuralFlood: an AI-driven flood susceptibility index

The Problem of Effective Evacuation of the Population from Floodplains under Threat of Flooding: Algorithmic and Software Support with Shortage of Resources

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