Flood risk assessment using an indicator based approach combined with flood risk maps and grid data

Wang, Won-joon; Kim, Donghyun; Han, Heechan; Tak Kim, Kyung; Kim, Soojun; Soo Kim, Hung

doi:10.1016/j.jhydrol.2023.130396

Cited by 11 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is constructed by the evaluation model by selecting indicators based on risk theory and the research area to assess mountain torrent risk. The core of the indicator weight method is the determination of the weight [13]. The Analytic Hierarchy Process [14], Entropy Weight [15], and CRITIC Method [16] are commonly used methods for determining weights.…”

Section: Introductionmentioning

confidence: 99%

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Chu,

Song,

Chen

2024

Water

View full text Add to dashboard Cite

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction.

show abstract

Section: Introductionmentioning

confidence: 99%

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Chu,

Song,

Chen

2024

Water

View full text Add to dashboard Cite

show abstract

“…Floods are one of the most common natural disasters, which affect a wider range of people than other natural disasters. Every year, floods cause huge disasters, significant economic losses, and human casualties [1,2]; Over the past decade, many studies [3][4][5]; have shown that the leading cause of death in cities during floods is related to roads, as the pathway of fast-moving flooding water. Globally, the socio-economic vulnerability to flood hazards is on the rise, as evidenced by the significant damages inflicted by recent major flooding events in various countries [6,7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Self-Rescue Possibilities for Pedestrians in the Aftermath of Destabilization during a Flood Event

Guo,

Wang,

Fang

et al. 2024

Water

View full text Add to dashboard Cite

Human safety is paramount in flood disasters. Current research indicates that the majority of fatalities in such disasters are due to people moving in water. Existing studies on human stability in floods have primarily focused on the static resistance of a standing posture against water flow, neglecting the realistic scenario where people need to move and attempt self-rescue in the aftermath of destabilization. This paper introduces an analysis of the stability during the self-rescue process following a fall in floodwaters, providing insights into the baseline risks of human impact in floods. The self-rescue process is defined as the recovery to a standing position after a fall, segmented into four postures: sitting, kneeling, squatting, and standing. Additionally, considering the significant variability of the current method (D×v water depth multiplied by flow velocity) used to assess human stability in floods, this research thoroughly investigates previously undefined parameters, including submerged volume, frontal area, wet friction coefficient, and flow resistance coefficient. This leads to the development of a physically meaningful self-rescue risk assessment formula, which is validated against previous studies for accuracy, with the aim of contributing new insights to flood risk management and public education.

show abstract

Understanding the evolution trend of urban flood risk and resilience for better flood management

Chen,

Lei,

et al. 2024

Ecological Indicators

View full text Add to dashboard Cite

Flood risk assessment using an indicator based approach combined with flood risk maps and grid data

Cited by 11 publications

References 22 publications

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Analysis of Self-Rescue Possibilities for Pedestrians in the Aftermath of Destabilization during a Flood Event

Understanding the evolution trend of urban flood risk and resilience for better flood management

Contact Info

Product

Resources

About