scholarly journals Flood Susceptibility Analysis using Geographic Information System, Frequency Ratio and Relative Frequency Ratio Methods in San Pa Tong District, Chiang Mai, Thailand

2021 ◽  
Vol 20 (2) ◽  
pp. 134-156
Author(s):  
Worawit Suppawimut
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Frequency Ratio ◽  
Relative Frequency ◽  
Geographic Information ◽  
Chiang Mai ◽  
Susceptibility Analysis ◽  
Flood Susceptibility ◽  
System Frequency

scholarly journals Modelagem espacial de áreas suscetíveis a inundações baseada em um modelo multicritério hibrido e Sistema de Informação Geográfica: um estudo de caso aplicado a bacia do Rio Bengalas

2020 ◽  
Vol 13 (3) ◽  
pp. 1145
Author(s):  
Fabiano Peixoto Freiman ◽  
Camila De Oliveira Carvalho
Keyword(s):  
Information System ◽  
Fuzzy Logic ◽  
Geographic Information System ◽  
River Basin ◽  
Analytical Hierarchy Process ◽  
Rio De Janeiro ◽  
Geographic Information ◽  
Flood Susceptibility ◽  
Hierarchy Process

A identificação de áreas suscetíveis a inundações é essencial para o gerenciamento de desastres e definição de políticas públicas. O objetivo deste trabalho é a apresentação de um método para identificação de áreas suscetíveis a inundações através da integração de informações geográficas provenientes de técnicas do Sensoriamento Remoto, as ferramentas do Sistema de Informação Geográfica (SIG), a lógica Fuzzy e a aplicação de Métodos de Análise Multicritério (MAM) Analytical Hierarchy Process (AHP). Para atingir o objetivo foi proposto um estudo de caso, localizado na Bacia do Rio Bengalas, nos municípios de Nova Friburgo e Bom Jardim (Região Serrana do Rio de Janeiro). A modelagem espacial multicritério foi realizada a partir da seleção de um conjunto de dados composto por informações geomorfológicas, hidrológicas e de uso e ocupação do solo. Como resultado, obteve-se um mapa de suscetibilidade a inundações para a região. A coerência do modelo gerado foi verificada a partir do histórico de inundações da bacia do Rio Bengalas. A metodologia, apresentou-se eficiente e adequada para a determinação de áreas suscetíveis a inundações, prevendo com sucesso a distribuição espacial de áreas com riscos a inundações.  Spatial modelling of flood-susceptible areas based on a hybrid multi-criteria model and Geographic Information System: a case study applied to the Bengalas River basin A B S T R A C TThe identification of areas susceptible to flooding is essential for disaster management and public policy making. The objective of this work is the presentation of a method for the identification of areas susceptible to floods through the integration of geographic information from Remote Sensing techniques, Geographic Information System (GIS) tools, Fuzzy logic and the application of Multicriteria Analysis Methods (MAM) Analytical Hierarchy Process (AHP). In order to achieve the objective, a case study was proposed, located in the Bengalas River Basin, in the municipalities of Nova Friburgo and Bom Jardim (Mountain Region of Rio de Janeiro). Multicriteria spatial modeling was performed by selecting a data set composed of geomorphological, hydrological and land use information. As a result, a flood susceptibility map was obtained for the region. The coherence of the generated model was verified from the flood history of the Bengalas River basin. The methodology was efficient and adequate for the determination of areas susceptible to floods, successfully predicting the spatial distribution of areas at risk of flooding.Keywords: flood susceptibility. Fuzzy logic. MAM. AHP. GIS. 

Flood Vulnerability, Risk, and Susceptibility Assessment

2020 ◽  
pp. 1-27
Author(s):  
Mohd Talha Anees ◽  
Ahmad Farid Bin Abu Bakar ◽  
Lim Hwee San ◽  
Khiruddin Abdullah ◽  
Mohd Nawawi Mohd Nordin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flood Risk ◽  
Geographic Information ◽  
Susceptibility Assessment ◽  
Flood Vulnerability ◽  
Local Conditions ◽  
Flood Susceptibility ◽  
Flood Risk Mapping

Flood can be assessed through flood vulnerability, risk, and susceptibility analysis using remote sensing, geographic information system, and hydrological modelling. In this chapter, different stages, complexities, and processes of flood vulnerability, risk, and susceptibility assessment were discussed. The study reveals that flood vulnerability should be assessed based on four aspects: physical, social, economic, and environmental. Flood risk should be assessed by three stages: risk analysis, disaster relief, and preparedness, whereas flood susceptibility assessment involves three processes. Overall, it was found that the responsible factors vary as per the local conditions, which need to be carefully analyzed and selected. Furthermore, the role of remote sensing and geographic information system in flood risk management were found important especially in flood risk mapping and in the selection of responsible flooding factors.

scholarly journals Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools

2018 ◽  
Vol 10 (3) ◽  
pp. 648 ◽  
Author(s):  
Sunmin Lee ◽  
Saro Lee ◽  
Moung-Jin Lee ◽  
Hyung-Sup Jung
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Geographic Information ◽  
Urban Flood ◽  
Flood Susceptibility ◽  
Gis Tools ◽  
Susceptibility Maps ◽  
Flooded Area ◽  
Using Data

Using geographic information system (GIS) tools and data-mining models, this study analyzed the relationships between flood areas and correlated hydrological factors to map the regional flood susceptibility of the Seoul metropolitan area in South Korea. We created a spatial database of data describing factors including topography, geology, soil, and land use. We used 2010 flood data for training and 2011 data for model validation. Frequency ratio (FR) and logistic regression (LR) models were applied to 2010 flood data to determine the relationships between the flooded area and its causal factors and to derive flood-susceptibility maps, which were substantiated using the area flooded in 2011 (not used for training). As a result of the accuracy validation, FR and LR model results were shown to have 79.61% and 79.05% accuracy, respectively. In terms of sustainability, floods affect water health as well as causing economic and social damage. These maps will provide useful information to decision makers for the implementation of flood-mitigation policies in priority areas in urban sustainable development and for flood prevention and management. In addition to this study, further analysis including data on economic and social activities, proximity to nature, and data on population and building density, will make it possible to improve sustainability.

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