scholarly journals GIS-Based Flood Susceptibility Mapping Using Statistical Index and Weighting Factor Models

2021 ◽  
Vol 19 (6) ◽  
pp. 1-13
Author(s):  
Worawit Suppawimut ◽  
Keyword(s):  
Land Use ◽  
Weighting Factor ◽  
Susceptibility Mapping ◽  
Slope Aspect ◽  
Economic Losses ◽  
Soil Drainage ◽  
Topographic Wetness Index ◽  
Statistical Index ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are one of the most devastating natural hazards, causing deaths, economic losses, and destruction of property. Flood susceptibility maps are an essential tool for flood mitigation and preparedness planning. This study mapped flood susceptibility using statistical index (SI) and weighting factor (WF) models in San Pa Tong District, Chiang Mai Province, Thailand. The conditioning factors used to perform flood susceptibility mapping were elevation, slope, aspect, curvature, topographic wetness index, stream power index, rainfall, distance from rivers, stream density, soil drainage, land use, and road density. The flood data were randomly classified as training data for mapping (70% of data) and testing data for model validation (30% of data). The results revealed that the SI and WF models classified 49.49% and 51.74% of the study area, respectively, as very highly susceptible to flooding. In the WF model, the factors with the greatest influence were land use, soil drainage, and elevation. The validation of the models using the area under the curve revealed that the success rates of the SI and WF models were 91.80% and 93.06%, while the prediction rates were 92.05% and 93.52%, respectively. The results from this study can be useful for local authorities in San Pa Tong District for flood preparedness and mitigation.

scholarly journals Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

2019 ◽  
Vol 11 (19) ◽  
pp. 5426 ◽  
Author(s):  
Saeid Janizadeh ◽  
Mohammadtaghi Avand ◽  
Abolfazl Jaafari ◽  
Tran Van Phong ◽  
Mahmoud Bayat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flash Flood ◽  
Susceptibility Mapping ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Flood Events ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

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 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 Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

2021 ◽  
Vol 13 (14) ◽  
pp. 2786
Author(s):  
Roya Narimani ◽  
Changhyun Jun ◽  
Saqib Shahzad ◽  
Jeill Oh ◽  
Kyoohong Park
Keyword(s):  
High Risk ◽  
Hybrid Method ◽  
Satellite Images ◽  
Controlling Factors ◽  
Susceptibility Mapping ◽  
Low Risk ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
Flood Maps

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data

2021 ◽  
Author(s):  
Zsófia Adrienn Kovács ◽  
János Mészáros ◽  
Mátyás Árvai ◽  
Annamária Laborczi ◽  
Gábor Szatmári ◽  
...  
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Hyperspectral Imagery ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Soil Carbon Content ◽  
Space Agency ◽  
Italian Space Agency ◽  
Agricultural Areas ◽  
Imagery Data

&lt;p&gt;The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.&lt;/p&gt;&lt;p&gt;The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.&lt;/p&gt;&lt;p&gt;The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.&lt;/p&gt;&lt;p&gt;Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km&lt;sup&gt;2&lt;/sup&gt; in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.&lt;/p&gt;&lt;p&gt;We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.&lt;/p&gt;&lt;p&gt;For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgment:&lt;/strong&gt; Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFT&amp;#214;-20-B-0022). Our project carried out using PRISMA Products, &amp;#169; of the Italian Space Agency (ASI), delivered under an ASI License to use.&lt;/p&gt;

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