Use of LSPIV in assessing urban flash flood vulnerability

Urban growth, extreme climate, and mismanagement are crucial controlling factors that affect flood vulnerability at wadi catchments. Therefore, this study attempts to understand the impacts of these three factors on the flash flood vulnerability in different climatic regions in Egypt. An integrated approach is presented to evaluate the urban growth from 1984 to 2019 by using Google Images and SENTINEL-2 data, and to develop hazard maps by using a rainfall-runoff-inundation model (RRI). Annual rainfall trend analysis was performed to evaluate the temporal variability trend. The hazard maps that were created were classified into three categories (low, medium, and high) and integrated with the urban growth maps to evaluate the impacts on the flood-vulnerable areas. The results show a significant increase in urban growth resulting in an increase of prone areas for flood hazards over time. However, the degree of this hazard is mainly related to growth directions. Mismanagement affects urban growth directions in both planned and unplanned growth, whether by loss of control over unplanned growth or by deficiencies in approved plans. The rainfall analysis showed that there is no explicit relationship to increases or decreases in the flood vulnerable areas. An urban planning approach is recommended for risk reduction management based on a comprehensive study considering such factors.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8070297 ◽

2019 ◽

Vol 8 (7) ◽

pp. 297 ◽

Cited By ~ 14

Author(s):

Junnan Xiong ◽

Jin Li ◽

Weiming Cheng ◽

Nan Wang ◽

Liang Guo

Keyword(s):

Support Vector Machine ◽

Vulnerability Assessment ◽

Flash Flood ◽

Flash Floods ◽

Support Vector ◽

Land Area ◽

Analytic Hierarchy ◽

Machine Model ◽

Flood Vulnerability ◽

Development Processes

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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Assessing flash flood vulnerability using a multi-vulnerability approach

E3S Web of Conferences ◽

10.1051/e3sconf/20160708004 ◽

2016 ◽

Vol 7 ◽

pp. 08004

Author(s):

Konstantinos Karagiorgos ◽

Thomas Thaler ◽

Fotis Maris ◽

Sven Fuchs

Keyword(s):

Flash Flood ◽

Flood Vulnerability

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Assessment of flash flood vulnerability zonation through Geospatial technique in high altitude Himalayan watershed, Himachal Pradesh India

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2018.09.001 ◽

2018 ◽

Vol 12 ◽

pp. 35-47 ◽

Cited By ~ 7

Author(s):

Sapna Bisht ◽

Smita Chaudhry ◽

Subrat Sharma ◽

Sandeep Soni

Keyword(s):

High Altitude ◽

Flash Flood ◽

Himachal Pradesh ◽

Flood Vulnerability ◽

Geospatial Technique

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Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Water ◽

10.3390/w11102116 ◽

2019 ◽

Vol 11 (10) ◽

pp. 2116 ◽

Cited By ~ 1

Author(s):

Mihnea Cristian Popa ◽

Daniel Peptenatu ◽

Cristian Constantin Drăghici ◽

Daniel Constantin Diaconu

Keyword(s):

Risk Management ◽

Flash Flood ◽

Flood Hazard ◽

Flash Floods ◽

Hazard Mapping ◽

Flood Vulnerability ◽

River Catchment ◽

Potential Index ◽

Vulnerability Maps ◽

Two Indices

The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

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