Estimation of flood risk index considering the regional flood characteristics: a case of South Korea

Jung, Younghun; Shin, Youngsoo; Jang, Chun Hwa; Kum, Donghyuk; Kim, Young Sug; Lim, Kyoung Jae; Kim, Hyun Bae; Park, Tae Sun; Lee, Seung Oh

doi:10.1007/s10333-014-0430-6

Cited by 9 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Asia, Jung et al constructed the flood risk index (FRI), considering the natural factors, social factors, administrative and economic factors, and facility factors. They found that the FRI was high in the southeastern coastal region and basins of the two biggest rivers in South Korea [49]. Shaikh et al believed that the education level and environmental awareness of the coastal area people has a close relationship with the activities during flood disaster in flood disaster risk reduction and adaptation in Bangladesh [50].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Agricultural Flood Risk Assessment Using Vulnerability Surface and Copula Functions

Zhang

Liu

Guo

et al. 2018

Water

View full text Add to dashboard Cite

Agricultural flood disaster risk assessment plays a vital role in agricultural flood disaster risk management. Extreme precipitation events are the main causes of flood disasters in the Midwest Jilin province (MJP). Therefore, it is important to analyse the characteristics of extreme precipitation events and assess the flood risk. In this study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method was used to determine the threshold of extreme precipitation events. The total duration of extreme precipitation and the total extreme precipitation were selected as flood indicators. The copula functions were then used to determine the joint distribution to calculate the bivariate joint return period, which is the flood hazard. Historical data and flood indicators were used to build an agricultural flood disaster vulnerability surface model. Finally, the risk curve for agricultural flood disasters was established to assess the flood risk in the MJP. The results show that the proposed approaches precisely describe the joint distribution of the flood indicators. The results of the vulnerability surface model are in accordance with the spatiotemporal distribution pattern of the agricultural flood disaster loss in this area. The agricultural flood risk of the MJP gradually decreases from east to west. The results provide a firm scientific basis for flood control and drainage plans in the area.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantitative Agricultural Flood Risk Assessment Using Vulnerability Surface and Copula Functions

Zhang

Liu

Guo

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…Investments in flood control structures and flood risk management also will be needed in areas where climate change will increase the frequency and severity of floods [108,125,126]. Investments in education, training, and international exchange will be needed to promote optimization of systems that include irrigation, drainage, and flood control components [127,128].…”

Section: Regional Adaptation Effortsmentioning

confidence: 99%

Managing Water and Soils to Achieve Adaptation and Reduce Methane Emissions and Arsenic Contamination in Asian Rice Production

Wichelns

2016

Water

View full text Add to dashboard Cite

Rice production is susceptible to damage from the changes in temperature and rainfall patterns, and in the frequency of major storm events that will accompany climate change. Deltaic areas, in which millions of farmers cultivate from one to three crops of rice per year, are susceptible also to the impacts of a rising sea level, submergence during major storm events, and saline intrusion into groundwater and surface water resources. In this paper, I review the current state of knowledge regarding the potential impacts of climate change on rice production and I describe adaptation measures that involve soil and water management. In many areas, farmers will need to modify crop choices, crop calendars, and soil and water management practices as they adapt to climate change. Adaptation measures at the local, regional, and international levels also will be helpful in moderating the potential impacts of climate change on aggregate rice production and on household food security in many countries. Some of the changes in soil and water management and other production practices that will be implemented in response to climate change also will reduce methane generation and release from rice fields. Some of the measures also will reduce the uptake of arsenic in rice plants, thus addressing an important public health issue in portions of South and Southeast Asia. Where feasible, replacing continuously flooded rice production with some form of aerobic rice production, will contribute to achieving adaptation objectives, while also reducing global warming potential and minimizing the risk of negative health impacts due to consumption of arsenic contaminated rice.

show abstract

“…Various approaches have been proposed to evaluate flood disaster risk based on the susceptibility of the system and hydrology [8][9][10]. The artificial neural network (ANN) is one of the most implemented machine-learning techniques in engineering risk assessment [11].…”

Section: Introductionmentioning

confidence: 99%

A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment

Khoirunisa

Liu

2021

IJERPH

View full text Add to dashboard Cite

This article presents a geographic information system (GIS)-based artificial neural network (GANN) model for flood susceptibility assessment of Keelung City, Taiwan. Various factors, including elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index, were generated using a digital elevation model and LANDSAT 8 imagery. Historical flood data from 2015 to 2019, including 307 flood events, were adopted for a comparison of flood susceptibility. Using these factors, the GANN model, based on the back-propagation neural network (BPNN), was employed to provide flood susceptibility. The validation results indicate that a satisfactory result, with a correlation coefficient of 0.814, was obtained. A comparison of the GANN model with those from the SOBEK model was conducted. The comparative results demonstrated that the proposed method can provide good accuracy in predicting flood susceptibility. The results of flood susceptibility are categorized into five classes: Very low, low, moderate, high, and very high, with coverage areas of 60.5%, 27.4%, 8.6%, 2.5%, and 1%, respectively. The results demonstrate that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area including the financial center of the city, can be categorized as high to very high flood susceptibility zones.

show abstract

Estimation of flood risk index considering the regional flood characteristics: a case of South Korea

Cited by 9 publications

References 18 publications

Quantitative Agricultural Flood Risk Assessment Using Vulnerability Surface and Copula Functions

Quantitative Agricultural Flood Risk Assessment Using Vulnerability Surface and Copula Functions

Managing Water and Soils to Achieve Adaptation and Reduce Methane Emissions and Arsenic Contamination in Asian Rice Production

A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment

Contact Info

Product

Resources

About