scholarly journals MAPEAMENTO DE SUSCETIBILIDADE À INUNDAÇÃO UTILIZANDO O MÉTODO DA RAZÃO DE FREQUÊNCIA APLICADO À BACIA DO RIACHO FUNDO - DISTRITO FEDERAL

2021 ◽  
Vol 9 (1) ◽  
pp. 148
Author(s):  
Hugo Leonardo Oliveira Chaves ◽  
Maria Elisa Leite Costa ◽  
Sérgio Koide ◽  
Tati De Almeida ◽  
Rejane Ennes Cicerelli
Keyword(s):  
Statistical Analysis ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Training Dataset ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Flood Susceptibility ◽  
Distrito Federal ◽  
Flood Susceptibility Mapping

<p>O mapeamento de suscetibilidade à inundação é importante para o manejo da dinâmica do uso do solo e, consequentemente, da hidrologia urbana local. O presente estudo produziu o mapa de suscetibilidade à inundação na Bacia do Riacho Fundo, Distrito Federal, utilizando o método estatístico bivariado Razão de Frequência (<em>Frequency Ratio</em>), com 30 pontos de inundação observados em 2018 como pontos de treinamento (71%) e outros 12 pontos de inundação (29%) como pontos de validação para desenvolvimento do modelo. O modelo é composto de 12 fatores de influência: declividade, curvatura, aspecto, hipsometria, distância dos rios, índice de potência de escoamento, índice de transporte de sedimento, índice topográfico de umidade, índice de rugosidade do terreno, índice de escoamento superficial, uso e cobertura do solo e geologia. Todas as variáveis com um tamanho de pixel de 12,5 m x 12,5 m. Os fatores de uso e cobertura do solo e geologia local mostraram-se os mais influentes no modelo. A validação do modelo foi realizada utilizando o método da área sob a curva, com uma acurácia de 85,75%. O estudo mostra que o método pode ser usado para auxiliar no estudo de planos de controle e mitigação de inundação em centros urbanos, como a locação preliminar de bacias de detenção.</p><p><strong>Palavras-chave</strong>: suscetibilidade, inundação, mapeamento, razão de frequência, geoprocessamento.</p><p> </p><p align="center">FLOOD SUSCEPTIBILITY MAPPING USING THE FREQUENCY RATIO METHOD APPLIED TO THE RIACHO FUNDO BASIN - FEDERAL DISTRICT</p><p class="Default"><strong>Abstract</strong><strong></strong></p><p>Flood susceptibility mapping is important to the management of the urban hydrological dynamic and to the studies conducted to prevent the flood-based problems. This study has produced a flood susceptibility map using a bivariate statistical analysis named frequency ratio (FR) model applied in the Riacho Fundo catchment, with 30 flooding locations (71%) for statistical analysis as training dataset and 12 remaining points (29%) were applied to validate the developed model. Twelve conditioning factors were considered in this study: slope, curvature, aspect, elevation, distance to river, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), terrain roughness index (TRI), superficial runoff index, land use/land cover (LULC) and geology. All these variables were resampled into 12.5×12.5 m pixel size. The model showed LULC and geology as the most influential factors in flooding. The AUC for success rate was 85.75% with the training points. The study shows the method can be used in studies of plans to mitigate and control flooding in urban centers, as preliminary lease of ponds.</p><p><strong>Keywords</strong>: susceptibility, flooding, mapping, frequency ratio, geoprocessing.</p>

scholarly journals A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China

2018 ◽  
Vol 11 (1) ◽  
pp. 62 ◽  
Author(s):  
Yi Wang ◽  
Haoyuan Hong ◽  
Wei Chen ◽  
Shaojun Li ◽  
Dragan Pamučar ◽  
...  
Keyword(s):  
Decision Making ◽  
Area Under The Curve ◽  
Analytic Network Process ◽  
Susceptibility Mapping ◽  
Collective Decision ◽  
Susceptibility Map ◽  
Hybrid Technique ◽  
Collective Decision Making ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of this work is to investigate the use a novel hybrid technique by integrating multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM), which is constructed by ensemble of decision making trial and evaluation laboratory (DEMATEL), analytic network process, weighted linear combinations (WLC) and interval rough numbers (IRN) techniques in the case study at Shangyou County, China. Specifically, we improve the DEMATEL method by applying IRN to determine connections in the network structure based on criteria and to accept imprecisions during collective decision making. The application of IRN can eliminate the necessity of additional information to define uncertain number intervals. Therefore, the quality of the existing data during collective decision making and experts’ perceptions that are expressed through an aggregation matrix can be retained. In this work, eleven conditioning factors associated with flooding were considered and historical flood locations were randomly divided into the training (70% of the total) and validation (30%) sets. The flood susceptibility map validates a satisfactory consistency between the flood-susceptible areas and the spatial distribution of the previous flood events. The accuracy of the map was evaluated by using objective measures of receiver operating characteristic (ROC) curve and area under the curve (AUC). The AUC values of the proposed method coupling with the WLC fuzzy technique for aggregation and flood susceptibility index are 0.988 and 0.964, respectively, which proves that the WLC fuzzy method is more effective for FSM in the study area. The proposed method can be helpful in predicting accurate flood occurrence locations with similar geographic environments and can be effectively used for flood management and prevention.

scholarly journals A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7653 ◽  
Author(s):  
Mahyat Shafapour Tehrany ◽  
Lalit Kumar ◽  
Farzin Shabani
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Belief Function ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Technique ◽  
Evidential Belief Function ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
The Individual

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM—radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

scholarly journals PRELIMINARY INVESTIGATIONS ON FLOOD SUSCEPTIBILITY MAPPING IN ANKARA (TURKEY) USING MODIFIED ANALYTICAL HIERARCHY PROCESS (M-AHP)

2018 ◽  
Vol XLII-5 ◽  
pp. 361-365 ◽  
Author(s):  
B. Sozer ◽  
S. Kocaman ◽  
H. A. Nefeslioglu ◽  
O. Firat ◽  
C. Gokceoglu
Keyword(s):  
Urban Areas ◽  
Flood Hazard ◽  
Analytic Solutions ◽  
Drainage Density ◽  
Susceptibility Mapping ◽  
Topographic Wetness Index ◽  
Flood Susceptibility ◽  
Spatial Datasets ◽  
Flood Susceptibility Mapping ◽  
Hierarchy Process

<p><strong>Abstract.</strong> Susceptibility mapping for disasters is very important and provides the necessary means for efficient urban planning, such as site selection and the determination of the regulations, risk assessment and the planning of the post-disaster stage, such as emergency plans and activities. The main purpose of the present study is to introduce the preliminary results of an expert based flood susceptibility mapping approach applied in urban areas in case of Ankara, Turkey. The proposed approach is based on Modified Analytic Hierarchy Process (M-AHP), which is an expert-based algorithm and provides data based modeling. The existing spatial datasets are evaluated in the decision process and the specified number of decision points according to the degree desired can be formed. The parameter priorities can be identified at the beginning of the modeling with this approach by the responsible expert. The spatial datasets used in the modeling and mapping process have been provided by the General Directorate of Mapping (HGM). Additionally, the slope gradient of topography, drainage density, and topographic wetness index of the site being one of the second derivatives of topography have been evaluated to identify the main conditioning factors controlling water accumulation on ground. Considering the uncertainties in flood hazard assessment and limitations in sophisticated analytic solutions, the proposed methodology could be evaluated to be an efficient tool to detect the most influential parameters representing the flood vulnerability and assessing the mitigation applications in urban environment.</p>

scholarly journals Integration of Information Theory, Fractal Theory and Statistical Analyses for Landslide Susceptibility Mapping at Regional Scales

2022 ◽  
Author(s):  
Xiaolong Deng ◽  
Guangji Sun ◽  
Naiwu He ◽  
Yonghua Yu
Keyword(s):  
Information Theory ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Fractal Theory ◽  
Spatial Association ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Slope Aspect ◽  
Ratio Method

Abstract A new model, integrating information theory, fractal theory and statistical model for accurate landslide susceptibility mapping (LSM) at regional scales, has been proposed. In this model, landslide conditional factors are firstly classified with an optimal number of classes, which is determined by maximizing their information coefficients estimated from Shannon’s entropy model. The spatial association between influencing factors and induced landslides has been measured by introducing the variable fractal dimension method (VFDM). The VFDM approach fully considers the characteristics of landslide fractal distribution. Then the fractal dimensions (\(D\)) are calculated to provide multiple factors with various numerical weights. The proposed model eventually combines the landslide frequency ratio (\(fr\)) of each factor with corresponding weight to achieve spatial prediction of landslides, illustrated by an example area in China. In the study area, 500 landslides have been identified by aerial photograph interpretation, extensive field investigations, historical and bibliographical landslide data. In the model, these landslides are randomly split into a training dataset (70 %)and a validating dataset (30 %) Seven factors are recognized and analyzed by frequency ratio (FR) method, including lithology, distance to fault, altitude, slope, aspect, distance to stream and distance to the road. The receiver operating characteristic curve (AUROC) has been adopted to compare and validate the model results. Results show that the proposed landslide model achieved a more accurate prediction with AUROC equal to 0.8467, over-performing than the conventional frequency ratio method (AUROC=0.8088). According to the final prognostic landslide susceptibility map, 16.37 % f the study area shows very high and high susceptibility, accounting for 63.55 % f the entire landslides. Evaluation of relative factor importance based on a one-by-one factor removal test indicates that the lithology factor contributes unique information for landslides. In conclusion, the example demonstrates that the proposed framework is promising for further improvement of LSM.

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