Flood loss prediction of coastal city based on GM-ANN

Cui, Pengzhan; Guan, Yeqing; Zhu, Ying

doi:10.1109/gsis.2017.8077700

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…According to the statistics, Shenzhen's economic losses caused by floods in 2014 exceeded 254 million yuan. Even more frightening, some experts [23] predict that this figure will surpass 257 million yuan by 2020 and 3.09 million in 2028. For the foreseeable future, Shenzhen will still be a Chinese "Big City" under the threat of flood disasters, with huge potential economic losses.…”

Section: Study Areamentioning

confidence: 99%

Affect Path to Flood Protective Coping Behaviors Using SEM Based on a Survey in Shenzhen, China

Huang

Cao

Wang

et al. 2020

IJERPH

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The initial concept of flood control has gradually shifted to flood risk management which emphasizes more public participation. Therefore, understanding the public’s protective coping behavioral patterns to floods is significant, and can help improve the effectiveness of public participation and implementation of flood-mitigation measures. However, the quantitative effect of socio-demographic factors on flood risk perception and behaviors is not clear. In this study, the socio-demographic factors are included to explore the quantitative relationship with and the affect path to flood protective coping behaviors with socio-demographic factors are studied. Shenzhen City in China is chosen as the study area, which suffers frequent urban floods every year. Questionnaire surveys are conducted in five flood-prone communities there, and 339 valid questionnaires were collected. The correlations between flood risk perception, flood risk knowledge, flood risk attitude, socio-demographic factors, and protective coping behaviors are analyzed firstly. A structural equation model (SEM) about these factors is then established to verify the correctness of hypothetical paths and discover new paths. The results indicates that socio-demographic factors and flood risk perception do not have impacts on protective coping behaviors directly, but are mediated by flood risk knowledge and flood risk attitude. Flood risk attitude is an important factor that affects protective coping behaviors directly. Moreover, two affect paths to flood protective coping behaviors are proposed. The findings of Shenzhen city in this study can be extended to other cities with similar characteristics, providing support for conducting effective flood mitigation measures.

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Section: Study Areamentioning

confidence: 99%

Affect Path to Flood Protective Coping Behaviors Using SEM Based on a Survey in Shenzhen, China

Huang

Cao

Wang

et al. 2020

IJERPH

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“…The AHP can rank alternative solutions based on several criteria through discrete or continuous pairwise comparisons to address decision-making problems [141], and today it is seldom used alone but, mostly, combined with other flood risk assessment tools to perceive the risk. For example, Cui (2017) considered flood factors in coastal cities, such as the geological deposition rate, rising sea levels, precipitation, the length of urban drainage pipes, annual GDP, and population, utilizing the AHP, Grey model (GM), and artificial neural network (ANN) to predict flood losses [142]. Currently, the application of MCDM in the field of composite flood risk perception is limited, but it has great potential for development.…”

Section: Integrated Hazard Risk Mappingmentioning

confidence: 99%

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Sun,

Zhang,

Ruan

et al. 2024

Remote Sensing

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Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders.

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“…According to local statistics, it can be found that almost every ten years, a severe flood disaster appears in Shenzhen. As Cui [35] predicted using artificial intelligence algorithm, the economic losses caused by floods in Shenzhen will exceed 257 million RMB by 2020 and 309 million RMB by 2028. Therefore, there is no doubt that flood risk in Shenzhen is extremely high.…”

Section: Study Areamentioning

confidence: 99%

Influential Factors Affecting Protective Coping Behaviors of Flood Disaster: A Case Study in Shenzhen, China

Cao

Yang

Huang

et al. 2020

IJERPH

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As the risk of urban flooding increases worldwide, floods seriously endanger the safety of people’s lives and property. Understanding the protective coping behaviors of the public in flood disasters is crucial to the implementation of effective flood mitigation measures and flood risk management. In this study, influential factors affecting protective coping behaviors in the face of flood disasters were identified, and the effects of these factors were discussed as well. Shenzhen City in China was selected as the study area, in which a questionnaire survey of 339 respondents was carried out in three flood-prone districts. Correlation analysis was conducted to preselect potential influential factors. Then, two linear regression models were established to identify main influential factors and to explore the interaction effects of these factors. The results indicated that age, monthly income, flood experience, trust in government and insurance willingness were main influential factors of protective coping behaviors. Trust in government had the highest positive correlation coefficient, while monthly income and age were negatively associated with protective coping behaviors. The interaction between insurance willingness and monthly income jointly affected protective coping behaviors of the public. The findings of this study could help authorities better understand the public’s intention to cope with flood and design effective risk reduction measures, not only for Shenzhen, but also for many other similar cities that facing with the same situation.

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Flood loss prediction of coastal city based on GM-ANN

Cited by 4 publications

References 1 publication

Affect Path to Flood Protective Coping Behaviors Using SEM Based on a Survey in Shenzhen, China

Affect Path to Flood Protective Coping Behaviors Using SEM Based on a Survey in Shenzhen, China

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Influential Factors Affecting Protective Coping Behaviors of Flood Disaster: A Case Study in Shenzhen, China

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