Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region

Sarkar, Debabrata; Mondal, Prolay

doi:10.1007/s13201-019-1102-x

Cited by 170 publications

(67 citation statements)

References 71 publications

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“…Most of the previously used models were mainly focused on the hydrodynamic model, hydrological model, multi-criteria decision analysis (MCDA), statistical models (SM), and machine learning (ML) techniques, which are incorporated in the geographic information system (Singh and Kumar 2013;Pradhan 2014;Elkhrachy 2015;Vojtek and Vojteková 2016;Rosser et al 2017;Samanta et al 2018;Tiryaki and Karaca 2018;Liuzzo et al 2019;Santos et al 2019;Tehrany et al 2019a;Shahabi et al 2020). The most commonly used models and techniques concerning flood susceptibility mapping include frequency ratio (FR) (Rahmati et al 2015;Shafapour Tehrany et al 2017;Samanta et al 2018;Tehrany et al 2019a;Sarkar and Mondal 2020), analytical hierarchy process (AHP) (Elkhrachy 2015;Dahri and Abida 2017;Das 2018;Rahman et al 2019;Sepehri et al 2020a), shannon's entropy (Khosravi and Pourghasemi 2016;, weights of evidence (WoE) (Tehrany et al 2014b;Rahmati et al 2015;Shafapour Tehrany et al 2017;Costache 2019), artificial neural networks (ANN) (Kia et al 2012;Ruslan et al 2013;Elsafi 2014;Rahman et al 2019;Kordrostami et al 2020), fuzzy logic (Nandalal and Ratnayake 2011;Sahana and Patel 2019;Sepehri et al 2020b), support vector machines (Tehrany et al 2014b…”

Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

Preprint

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Flood is a typical natural disaster which every year causes enormous damage to the natural environment, buildings and victims worldwide. The efficiency of flooding depends upon numerous criteria: flood strength, amplitude, frequency, flow time, changes in plan and river geometry, etc. In this research, three statistical models such as the frequency ratio, Shannon's entropy, and weight of evidence have been used to identify flood susceptibility regions in the Kopai River Basin. In order to run these statistical models, flooded and non-flooded pixels were employed. The outcome of these three models demonstrates that the upper reach of the basin is characterised by non-flooded zones and the bottom reach of the basin is high flooded zones. The lower part of the basin is therefore more vulnerable than that of the upper portion. 11 thematic layers are employed to get the final output of flood susceptibility zones. e.g., soil type, normalised vegetation difference index, normalised water differential index, river lift, slope, drainage density, land usage and land cover, rainfall, soil moisture indices, surface roughness. 370 sample points have been taken as test data and finally receiver operating curves have been plotted for testing the degree of accuracy. The result of the validation reveals that the accuracy of the frequency ratio is 96.5%. Shannon entropy is 91.2% accurate and the weight of evidence is 97.1% accurate. Weight of evidence is therefore the best model for flood susceptibility zones identification for this Kopai basin.

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Section: Introductionmentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

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“…GIS technique is a suitable tool that facilitates the integration of data sets of rainfall, soil, topography, land use, drainage, and catchment flows to map flood vulnerable areas. The flood vulnerability maps are vital tools for flood assessment, mitigation, planning and control (Ayeni, 1998;Szewrański et al, 2018;Sarkar and Mondal 2020). This study assesses urban agricultural lands vulnerable to flooding in Makurdi, Benue State, Nigeria, using geospatial technology.…”

Section: Introductionmentioning

confidence: 99%

Empirical Model Valuation of Urban Agriculture Vulnerability to Flooding in Makurdi, Benue State, Nigeria

Kunda¹,

Jajere²,

Otabe³

et al. 2021

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For this study, geospatial technology was used to assess agricultural lands vulnerable to flooding in Makurdi, Benue State, Nigeria. Six thematic layers of factors influencing flood occurrences in the study area were generated from monthly rainfall, land use/cover, drainage density, soil, digital elevation model and slope. Pairwise comparison of the Analytical Hierarchy Process was used to derive the weights for each factor using expert’s judgements and literature. Weighted overlay model from the spatial analysis tool in the ArcGIS 10.4 environment was used to perform the vulnerability modelling. Expert’s judgement on the relative factors influencing flood in the study area was: rainfall (25%), elevation (22%), slope (20%), drainage density (13%), soil type (8%) and land use/cover (12%). The consistency ratio of the analysis was reasonable: (CR= 0.078). Results from the model demonstrated land vulnerability to urban agricultural flooding in the study area ranging from areas of very highly vulnerable to very low vulnerable areas, with farmlands along the floodplains of River Benue falls within the very highly vulnerable areas. The elements at Risk are; Farmland 537.6 (66.1%), Irrigation Land 40.5 (5.0%) and Built-up Land 125.8 (15.5%).

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“…With satellite images delivering accurate data at high temporal resolution and with such high spatial precision that the need for conducting field surveys has been significantly obliterated. This has resulted in a plethora of mapping studies such as landslide susceptibility mapping (Bragagnolo et al 2020a, b;Hu et al 2020;Sameen et al 2020;Sansare and Mhaske 2020;Tang et al 2020;Van Dao et al 2020;Wang et al 2020;Wu et al 2020), flood susceptibility mapping (Bui et al 2020;Chen et al 2020a;Costache and Bui 2020;Feloni et al 2020;Feng et al 2020;Mishra and Sinha 2020;Pourghasemi et al 2020;Sansare and Mhaske 2020;Sarkar and Mondal 2020), and forest fire susceptibility mapping (Abedi Gheshlaghi et al 2020;Ç olak and Sunar 2020;Rahimi et al 2020;Sevinc et al 2020;Venkatesh et al 2020), mineral potential mapping (de Quadros et al 2006) etc., employing RS for obtaining data for regions that were traditionally considered inaccessible. These studies have focused on generating zonation maps delineating the zones on the basis of their relative potential/susceptibility/vulnerability/proneness using a variety of statistical techniques such as weight-of-evidence (Mastere 2020;Zaheer et al 2020;Rahmati et al 2016;Kayastha et al 2012;Ozdemir 2011;Corsini et al 2009;de Quadros et al 2006;Lee and Choi 2004), frequency ratio (Sarkar and Mondal 2020;Rahmati et al 2016;Naghibi et al 2015;…”

Section: Introductionmentioning

confidence: 99%

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

Sachdeva

Kumar

2020

Stoch Environ Res Risk Assess

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In the drought prone district of Dholpur in Rajasthan, India, groundwater is a lifeline for its inhabitants. With population explosion and rapid urbanization, the groundwater is being critically over-exploited. Hence the current groundwater potential mapping study was undertaken to ascertain the areas that are more likely to yield a larger volume of groundwater against those areas that have poor groundwater potential and accordingly perpetuate the much needed damage control. Thematic layers for 14 groundwater influencing factors were considered for the study region, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), geology, soil, land use, normalized difference vegetation index (NDVI), surface temperature, precipitation, distance from roads, and distance from rivers. These were then subjected to an overlay operation, with the groundwater inventory which comprised of the locations of observational groundwater wells. The resulting geospatial database was then used to train two decision tree based ensemble models: gradient boosted decision trees (GBDT) and random forest (RF). The predictive performance of these models was then compared using various performance metrics such as area under curve (AUC) of receiver operating characteristics (ROC), sensitivity, accuracy, etc. It was found that GBDT (AUC: 0.79) outperformed RF (AUC: 0.71). The validated GBDT model was then used to construct the groundwater potential zonation map. The generated map showed that about 20.2% of the region has very high potential, while 22.6% has high potential to yield groundwater, and approximately 19.9-17.5% of the study region has very low to low groundwater potential.

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Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region

Cited by 170 publications

References 71 publications

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Empirical Model Valuation of Urban Agriculture Vulnerability to Flooding in Makurdi, Benue State, Nigeria

Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

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