District flood vulnerability index: urban decision-making tool

Nasiri, H.; Yusof, Mohd Johari Mohd; Ali, Thamer Ahmad Mohammad; Hussein, Mohd Kher

doi:10.1007/s13762-018-1797-5

Cited by 79 publications

(47 citation statements)

References 15 publications

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“…Compared with previous models, the SVM method not only avoids the influence of human factors, but also extends the information content [19]. In addition, the application of a comprehensive vulnerability assessment of flash floods can more accurately and locally identify the spatial patterns of vulnerable areas for flash floods in China [52]. Due to a lack of national data on resilience, the disaster reduction indicators only included susceptibility and coping ability.…”

Section: The Assessment Methodologymentioning

confidence: 99%

“…The spatial patterns of exposure revealed that areas with high population density, high GDP, and a high density of public infrastructure had a higher sensitivity to flash floods [52,55]. In the developed east and southeast coastal areas of China, due to the impact of dense populations, concentration of enterprises and institutions, and a high economic density, the region is extremely sensitive to flash floods.…”

Section: Exposure Disaster Reduction Capability and Vulnerability Amentioning

confidence: 99%

“…It was observed that high disaster reduction capabilities were mainly distributed in the northern, central, and southwestern regions of China, indicating that people can quickly cope with losses and have a strong resistance capacity to flash floods in these zones ( Figure 5). The spatial patterns of disaster reduction capabilities revealed that regions with high monitoring and early warning capabilities, high road densities, high river densities, and good medical rescue abilities had higher flash flood susceptibility and coping abilities [11,52]. As to the west areas of China, such as Tibet, Xinjiang, and Qinghai, people's coping ability and resistance to flash floods are low, mainly due to an outdated social economy and low self-rescue and medical rescue capabilities [58].…”

Section: Exposure Disaster Reduction Capability and Vulnerability Amentioning

confidence: 99%

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A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China

Xiong

Cheng

et al. 2019

IJGI

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Flash floods are one of the natural disasters that threaten the lives of many people all over the world every year. Flash floods are significantly affected by the intensification of extreme climate events and interactions with exposed and vulnerable socio-economic systems impede regional development processes. Hence, it is important to estimate the loss due to flash floods before the disaster occurs. However, there are no comprehensive vulnerability assessment results for flash floods in China. Fortunately, the National Mountain Flood Disaster Investigation Project provided a foundation to develop this proposed assessment. In this study, an index system was established from the exposure and disaster reduction capability categories, and is based on analytic hierarchy process (AHP) methods. We evaluated flash flood vulnerability by adopting the support vector machine (SVM) model. Our results showed 439 counties with high and extremely high vulnerability (accounting for 10.5% of the land area and corresponding to approximately 100 million hectares (ha)), 571 counties with moderate vulnerability (accounting for 19.18% of the land area and corresponding to approximately 180 million ha), and 1128 counties with low and extremely low vulnerability (accounting for 39.43% of the land area and corresponding to approximately 370 million ha). The highly-vulnerable counties were mainly concentrated in the south and southeast regions of China, moderately-vulnerable counties were primarily concentrated in the central, northern, and southwestern regions of China, and low-vulnerability counties chiefly occurred in the northwest regions of China. Additionally, the results of the spatial autocorrelation suggested that the “High-High” values of spatial agglomeration areas mainly occurred in the Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Chongqing, and Beijing areas. On the basis of these results, our study can be used as a proposal for population and building distribution readjustments, and the management of flash floods in China.

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Section: The Assessment Methodologymentioning

confidence: 99%

Section: Exposure Disaster Reduction Capability and Vulnerability Amentioning

confidence: 99%

Section: Exposure Disaster Reduction Capability and Vulnerability Amentioning

confidence: 99%

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A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China

Xiong

Cheng

et al. 2019

IJGI

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“…Simplification consisted of eliminating the second circulation of the results of the first round of surveys, considering it inoperative with adequate convergence of answers in the first round. The importance assigned by the experts to each of the variables was therefore considered in the MCA model (Table 1), reducing the subjectivity of the analysis and the results obtained [44]. Within the spatial MCA, up to 44 variables were evaluated (17 associated with flood hazard, 11 with flood exposure, and 16 with flood vulnerability), as can be seen in Figure 2.…”

Section: The Flood Risk Maps Of the Pricam Project (Mca And Gis)mentioning

confidence: 99%

Can the Quality of the Potential Flood Risk Maps be Evaluated? A Case Study of the Social Risks of Floods in Central Spain

Garrote

Gutiérrez-Pérez

Díez‐Herrero

2019

Water

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Calibration and validation of flood risk maps at a national or a supra-national level remains a problematic aspect due to the limited information available to carry out these tasks. However, this validation is essential to define the representativeness of the results and for end users to gain confidence in them. In recent years, the use of information derived from social networks is becoming generalized in the field of natural risks as a means of validating results. However, the use of data from social networks also has its drawbacks, such as the biases associated with age and gender and their spatial distribution. The use of information associated with phone calls to Emergency Services (112) can resolve these deficiencies, although other problems are still latent. For example, a bias does exist in the relationship between the size of the population and the number of calls to the Emergency Services. This last aspect determines that global regression models have not been effective in simulating the behavior of related variables (calls to Emergency Services-Potential Flood Risk). Faced with this situation, the use of local regression models (such as locally estimated scatterplot smoothing (LOESS)) showed satisfactory results in the calibration of potential flood risk levels in the Autonomous Community of Castilla-La Mancha (Spain). This provides a new methodological path to the calibration studies of flood risk cartographies at national and supra-national levels. The results obtained through LOESS local regression models allowed us to establish the correct relationship between categorized potential risk levels and the inferred potential risk. They also permitted us to define the cases in which said levels differed ostensibly and where potential risk due to floods assigned to those municipalities led to a lower level of confidence. Therefore, based on the number of calls to the Emergency Service, we can categorize those municipalities that should be the subject of a more detailed study and those whose classification should be revised in future updates. territory conditions the flood risk analysis approach [2]; each scale of work requires the use of different methods of analysis, and the results must satisfy different uses. Depending on the spatial extent of the analysis, de Moel et al. [2] propose four scales (supra-national, macro-scale or national, meso-scale or regional, and micro-scale or local). This does not mean they are isolated, because some of the analysis methodologies are valid for different scales (in many cases by using grouping techniques or by simplifying calculation processes). Approaches, analysis techniques, results, uncertainties, and processes used for validation of the results associated with each of these four working scales are included in de Moel et al. [2]. The processes used to validate results are key points that will require a greater effort in the future (regardless of the scale of work of the analysis), since they also determine the utility for end users [3]. Thus, de Moel et al. [2] r...

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“…However, the adverse flood hazard and its repercussion are increasing day by day posing serious threat to life and livelihood of its inhabitants. A host of factors such as climate change, incessant rainfall, haphazard land use pattern, population growth, and urbanization (3) have contributed substantially in transforming the hazard into a disaster accompanied by a rise in flood frequency (4) .…”

Section: Introductionmentioning

confidence: 99%

Application of geospatial technologies in flood hazard assessment of Dhemaji revenue circle, Assam

Boruah¹

2020

IJST

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Background: Land of monsoons, India has been triggered by flood and siltation annually through southwest and northeast monsoons along the floodplains of the country for millennia. Being one of the worst sufferers, Assam also experiences the severe effects of flood hazard as the region is drained by the mighty Brahmaputra and Barak basins. In this view, Dhemaji district located in the upper reach of Brahmaputra valley also bears the brunt of flood leaving massive imprints on people and landscape. Objective: This study is an attempt to identify the flood vulnerable zones and damages incurred in a flood period of Dhemaji revenue circle. Methodology: The study is based on both primary and secondary sources of information wherein the data and maps have been processed and prepared within the domain of GIS and remote sensing. Findings: The results indicate that the entire region is categorized into two flood hazard zones viz. least and highly vulnerable wherein the former covers 55.27% and latter 44.73% of the total area. The damages caused in respect to cropland and transportation network have also been estimated and few management strategies were recommended to cope with the hazard. Novelty: The existing work has used various physical factors wherein priority ranks are allotted to each based on their intensity towards flood occurrence. Thus, this study would help in identification of potential hazard zones and affected areas through mapping and modeling using geospatial techniques which are cost and time effective.

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District flood vulnerability index: urban decision-making tool

Cited by 79 publications

References 15 publications

A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China

A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China

Can the Quality of the Potential Flood Risk Maps be Evaluated? A Case Study of the Social Risks of Floods in Central Spain

Application of geospatial technologies in flood hazard assessment of Dhemaji revenue circle, Assam

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