scholarly journals Frequency ratio application for mapping flood susceptibility in Welang Watershed, East Java

2021 ◽  
Vol 930 (1) ◽  
pp. 012095
Author(s):  
R Aprilia ◽  
E Hidayah ◽  
D Junita K
Keyword(s):  
Frequency Ratio ◽  
Area Under Curve ◽  
Vegetation Index ◽  
Flood Hazard ◽  
Standardized Precipitation Index ◽  
Training Data ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Flood Susceptibility

Abstract Flood is one of the disaster threats downstream of Welang river, Pasuruan. A flood susceptibility map is needed to anticipate floods disasters. This research aimed to map flood Susceptibility in the Welang watershed using a Geographical Information System. In determining flood hazard, the Frequency Ratio (FR) approach was used. Flood locations were identified from the interpretation of field survey data as training data and model validation. The data were represented in a Digital Elevation Model (DEM) map, geological data, land use, river data, and Landsat Satellite Imagery and processed into a spatial database on the GIS platform. The factors that caused flooding consisted of Flood inventory, slope, Elevation, Topographic Wetness Index (TWI), Standardized Precipitation Index (SPI), Flow Accumulation, Distance to the river, River Density, Rainfall, Vegetation Index (NDVI), and Landuse. The map results with acceptable accuracy showed that the FR model gained an Area Under Curve (AUC) value of 90%, and the incidence for the Area Under Curve ( AUC ) was 93%. It is known that 1% of the flood-prone area is very high. The local Government can use the research to minimize the risk of flooding in the Welang watershed.

scholarly journals Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

2019 ◽  
Vol 11 (13) ◽  
pp. 1589 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
Himan Shahabi ◽  
Prasad Daggupati ◽  
Jan F. Adamowski ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vegetation Index ◽  
Slope Angle ◽  
Training Data ◽  
Multivariate Logistic Regression Model ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Validation Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

scholarly journals Comparison of Statistical and Analytical Hierarchy Process Methods on Flood Susceptibility Mapping: In a Case Study of the Tana Sub-basin in Northwestern Ethiopia

2021 ◽  
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamirat Mekuria ◽  
...  
Keyword(s):  
Analytical Hierarchy Process ◽  
Frequency Ratio ◽  
Flood Hazard ◽  
Area Under The Curve ◽  
Google Earth ◽  
Information Value ◽  
Susceptibility Map ◽  
Flood Hazards ◽  
Flood Susceptibility ◽  
Hierarchy Process

Abstract The sub-basin of Lake Tana is one of the most flood-prone areas in northwestern Ethiopia, which is affected by flood hazards. Flood susceptibility modeling in this area is essential for hazard reduction purposes. For this, the analytical hierarchy process (AHP), bivariate, and multivariate statistical methods were used. Using an intensive field survey, historical record, and Google Earth Imagery, 1404 flood locations were determined which are classified into 70% training datasets and 30% testing flood datasets using subset in the GIS tool. The statistical relationship between the probability of flood occurrence and eleven flood-driving factors is performed using the GIS tool. Then, the flood susceptibility map of the area is developed by summing all weighted factors using a raster calculator and classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The results for the area under the curve (AUC) are 99.1% for the frequency ratio model is better than 86.9% using AHP, 81.4% using the logistic regression model, and 78.2% using the information value model. Based on the AUC values, the frequency ratio (FR) model is relatively better followed by the AHP model for regional flood use planning, flood hazard mitigation, and prevention purposes.

scholarly journals Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: in a case study of Tana sub-basin in northwestern Ethiopia

2020 ◽  
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamirat Mekuria ◽  
...  
Keyword(s):  
Analytical Hierarchy Process ◽  
Frequency Ratio ◽  
Flood Hazard ◽  
Area Under The Curve ◽  
Google Earth ◽  
Information Value ◽  
Susceptibility Map ◽  
Flood Hazards ◽  
Flood Susceptibility ◽  
Hierarchy Process

Abstract. The sub-basin of Lake Tana is one of the most flood-prone areas in northwestern Ethiopia, which is affected by flood hazards. Flood susceptibility modeling in this area is essential for hazard reduction purposes. For this, the analytical hierarchy process (AHP), bivariate, and multivariate statistical methods were used. Using an intensive field survey, historical record, and Google Earth Imagery, 1404 flood locations were determined which are classified into 70 % training datasets and 30 % testing flood datasets using subset in the GIS tool. The statistical relationship between the probability of flood occurrence and eleven flood-driving factors is performed using the GIS tool. Then, the flood susceptibility map of the area is developed by summing all weighted factors using a raster calculator and classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The results for the area under the curve (AUC) are 99.1 % for the frequency ratio model is better than 86.9 % using AHP, 81.4 % using the logistic regression model, and 78.2 % using the information value model. Based on the AUC values, the frequency ratio (FR) model is relatively better followed by the AHP model for regional flood use planning, flood hazard mitigation, and prevention purposes.

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 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 EVALUATING THE VARIATIONS IN THE FLOOD SUSCEPTIBILITY MAPS ACCURACIES DUE TO THE ALTERATIONS IN THE TYPE AND EXTENT OF THE FLOOD INVENTORY

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 209-214 ◽  
Author(s):  
M. Sh. Tehrany ◽  
S. Jones
Keyword(s):  
Area Under Curve ◽  
Natural Hazard ◽  
Susceptibility Map ◽  
Inventory Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
Rapid Processing ◽  
The Impact ◽  
Measurement Approach

This paper explores the influence of the extent and density of the inventory data on the final outcomes. This study aimed to examine the impact of different formats and extents of the flood inventory data on the final susceptibility map. An extreme 2011 Brisbane flood event was used as the case study. LR model was applied using polygon and point formats of the inventory data. Random points of 1000, 700, 500, 300, 100 and 50 were selected and susceptibility mapping was undertaken using each group of random points. To perform the modelling Logistic Regression (LR) method was selected as it is a very well-known algorithm in natural hazard modelling due to its easily understandable, rapid processing time and accurate measurement approach. The resultant maps were assessed visually and statistically using Area under Curve (AUC) method. The prediction rates measured for susceptibility maps produced by polygon, 1000, 700, 500, 300, 100 and 50 random points were 63&amp;thinsp;%, 76&amp;thinsp;%, 88&amp;thinsp;%, 80&amp;thinsp;%, 74&amp;thinsp;%, 71&amp;thinsp;% and 65&amp;thinsp;% respectively. Evidently, using the polygon format of the inventory data didn’t lead to the reasonable outcomes. In the case of random points, raising the number of points consequently increased the prediction rates, except for 1000 points. Hence, the minimum and maximum thresholds for the extent of the inventory must be set prior to the analysis. It is concluded that the extent and format of the inventory data are also two of the influential components in the precision of the modelling.

scholarly journals A MAP MASH-UP APPLICATION: INVESTIGATION THE TEMPORAL EFFECTS OF CLIMATE CHANGE ON SALT LAKE BASIN

2016 ◽  
Vol XLI-B4 ◽  
pp. 221-226
Author(s):  
O. S. Kirtiloglu ◽  
O. Orhan ◽  
S. Ekercin
Keyword(s):  
Climate Change ◽  
Web Application ◽  
Vegetation Index ◽  
Salt Lake ◽  
Standardized Precipitation Index ◽  
Geographical Information ◽  
Lake Basin ◽  
Landsat Images ◽  
Google Maps ◽  
Normalised Difference Vegetation Index

The main purpose of this paper is to investigate climate change effects that have been occurred at the beginning of the twenty-first century at the Konya Closed Basin (KCB) located in the semi-arid central Anatolian region of Turkey and particularly in Salt Lake region where many major wetlands located in and situated in KCB and to share the analysis results online in a Web Geographical Information System (GIS) environment. 71 Landsat 5-TM, 7-ETM+ and 8-OLI images and meteorological data obtained from 10 meteorological stations have been used at the scope of this work. 56 of Landsat images have been used for extraction of Salt Lake surface area through multi-temporal Landsat imagery collected from 2000 to 2014 in Salt lake basin. 15 of Landsat images have been used to make thematic maps of Normalised Difference Vegetation Index (NDVI) in KCB, and 10 meteorological stations data has been used to generate the Standardized Precipitation Index (SPI), which was used in drought studies. For the purpose of visualizing and sharing the results, a Web GIS-like environment has been established by using Google Maps and its useful data storage and manipulating product Fusion Tables which are all Google’s free of charge Web service elements. The infrastructure of web application includes HTML5, CSS3, JavaScript, Google Maps API V3 and Google Fusion Tables API technologies. These technologies make it possible to make effective “Map Mash-Ups” involving an embedded Google Map in a Web page, storing the spatial or tabular data in Fusion Tables and add this data as a map layer on embedded map. The analysing process and map mash-up application have been discussed in detail as the main sections of this paper.

scholarly journals Mapping of Landslide Susceptible Zones by Using Frequency Ratios at Bluncong Subwatershed, Bondowoso Regency

UKaRsT ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 126
Author(s):  
Didik Efendi ◽  
Entin Hidayah ◽  
Akhmad Hasanuddin
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Local Governments ◽  
Vegetation Index ◽  
Data Interpretation ◽  
Geographical Information ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model ◽  
Frequency Ratios

Landslides are the disasters that frequently happen in Bluncong sub-watershed. These incidents have caused damage and malfunction of road infrastructure, bridges, and irrigation buildings. Therefore, it is important to anticipate landslides through mapping of landslide-susceptibility areas The objective of this study is to map landslide susceptibility at Bluncong sub watershed, Bondowoso, by using Geographical Information System and remote sensing. The landslide susceptibility analysis and mapping are conducted based on landslide occurrences with the Frequency Ratio approach. The landslide sites are identified from field survey data interpretation. Digital Elevation Model maps, geological data, land uses and rivers data, and Landsat 8 images are collected, processed, and then built into the GIS platform's spatial database. The selected factors that cause landslide occurrences are land use, distance to river, aspect, slope, elevation, curvature, and the vegetation index (NDVI). The results show that the accuracy of the map is acceptable. The frequency ratio model gained the area under curve (AUC) value of 0.79. It is found that 9.08% of the area has very high landslide susceptibility. Local governments can use this study's mapping results to minimize the risk at landslidesusceptible zones

scholarly journals Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets

2021 ◽  
Vol 13 (23) ◽  
pp. 4761
Author(s):  
Saeid Parsian ◽  
Meisam Amani ◽  
Armin Moghimi ◽  
Arsalan Ghorbanian ◽  
Sahel Mahdavi
Keyword(s):  
Remote Sensing ◽  
Analytical Hierarchy Process ◽  
Vegetation Index ◽  
Flood Hazard ◽  
Fuzzy Membership Function ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Topographic Wetness Index ◽  
Flood Hazard Map ◽  
Hierarchy Process

Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood hazard map with five classes of susceptible zones. The bi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) images, acquired before and on the day of the flood event, were used to evaluate the accuracy of the produced flood hazard map. The results indicated that 95.16% of the actual flooded areas were classified as very high and high flood hazard classes, demonstrating the high potential of this approach for flood hazard mapping.

scholarly journals A Deterministic Topographic Wetland Index Based on LiDAR-Derived DEM for Delineating Open-Water Wetlands

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2487
Author(s):  
Linlong Bian ◽  
Assefa M. Melesse ◽  
Arturo S. Leon ◽  
Vivek Verma ◽  
Zeda Yin
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Truth ◽  
Open Water ◽  
Training Data ◽  
Wetland Delineation ◽  
Topographic Wetness Index ◽  
Water Index

Wetlands play a significant role in flood mitigation. Remote sensing technologies as an efficient and accurate approach have been widely applied to delineate wetlands. Supervised classification is conventionally applied for remote sensing technologies to improve the wetland delineation accuracy. However, performing supervised classification requires preparing the training data, which is also considered time-consuming and prone to human mistakes. This paper presents a deterministic topographic wetland index to delineate wetland inundation areas without performing supervised classification. The classic methods such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Topographic Wetness Index were chosen to compare with the proposed deterministic topographic method on wetland delineation accuracy. The ground truth sample points validated by Google satellite imageries from four different years were used for the assessment of the delineation overall accuracy. The results show that the proposed deterministic topographic wetland index has the highest overall accuracy (98.90%) and Kappa coefficient (0.641) among the selected approaches in this study. The findings of this paper will provide an alternative approach for delineating wetlands rapidly by using solely the LiDAR-derived Digital Elevation Model.

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