Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India

Samanta, Ratan Kumar; Bhunia, Gouri Sankar; Shit, Pravat Kumar; Pourghasemi, Hamid Reza

doi:10.1007/s40808-018-0427-z

Cited by 142 publications

(49 citation statements)

References 50 publications

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“…Flood is the most prevalent and devastating natural disaster among all natural disasters that have adverse impacts on human health, natural and artificial environments [1,2]. Flood is a major risk to human life (loss of life, injury), assets (agriculture area, yield production, homes, is easy to apply and can produce acceptable risk analysis and mapping [9,26,35,36].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, some studies show that bivariate statistical models sometimes have a higher accuracy than machine learning models, which require huge amounts of data as training for better accuracy [40][41][42]. FR is the bivariate statistical method that can consider the correlation between dependent factors (historical flood points) and independent factors (flood-causative factors) [1,25,43]. FR models have been successfully applied to flood susceptibility and vulnerability assessments in different flood prone regions of the world [1,25,26].…”

Section: Introductionmentioning

confidence: 99%

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GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

2020

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Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate floodprone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

2020

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“…The present study proposes the computation of both indices which were highlighted in numerous studies aiming at determining the areas prone to these natural hazards. The outputs of the analysis make this study relevant, as other studies [32,35,36,38,40,47,72] propose the computation of only one index for creating hazard maps. The optimal split of the training and testing set used in the present paper was determined through successive testing using different split ratios-80-20 and 90-10.…”

Section: Discussionmentioning

confidence: 99%

“…The frequency ratio (FR) model represents a bivariate statistical method which is widely used in research for landslide and flood prediction mapping [24,29,31,32,47,63]. The present paper proposes the use of the FR model to map the areas prone to floods and flash-floods in the Buzău river catchment.…”

Section: Frequency Ratio Model (Fr)mentioning

confidence: 99%

“…The results of the abovementioned analyses were translated into a GIS environment, thus resulting in flood and flash-flood hazard maps. These methods are widely used in research in studies which aim to provide landslide or flood mapping [24,[31][32][33][34]. The performance validation of the proposed models has been done using receiver operating characteristic (ROC) curves.…”

Section: Introductionmentioning

confidence: 99%

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

Popa

Peptenatu

Drăghici

et al. 2019

Water

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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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Flood inundation mapping in data‐scarce areas: A case of Mbire District, Zimbabwe

Manyangadze

Mavhura

Mudavanhu

et al. 2022

Geography and Environment

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Floods are one of the most devastating weather‐related hazards that are affecting millions of people over the world every year. In some poor resource areas such as Mbire District in Zimbabwe, the floods are difficult to anticipate and prepare for. Hence the need for spatial modelling of the past flood events for effective response and management. This study modelled the flood extent and depth based on data from household surveys, transect walks and a digital elevation model (DEM). A sample of 304 households was used, with 70% for calibration and 30% for validation of the flood extent. Twenty‐four flood depth measurements obtained from transect walks were used to validate the modelled flood depths based on a linear regression model. The flood depth of the worst most recent flood (January 2015) at each household was combined with altitude from the DEM using the sum function, and the inverse distance weighting was applied to model the worst flood depth. The flood extent was considered as those areas where flood depth was higher than the DEM. Approximately 24% of the area was covered by floods. The modelled flood extent agreed reasonably well with what was reported during the survey (probability of detection 0.93 and accuracy level about 0.8). Most of the areas in the wards experienced flood depths greater than 2 m, especially along the major rivers. Such areas are dangerous for people, animals and properties such as boreholes, houses, schools and clinics located on the floodplain. These results can be used for planning purposes in preparing and responding to stages of the flood management cycle. However, there is a need for further research to improve the performance and applicability of the methodology applied in this study in other settings.

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Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India

Cited by 142 publications

References 50 publications

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan

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

Flood inundation mapping in data‐scarce areas: A case of Mbire District, Zimbabwe

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