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Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India

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Abstract

The primary objective of this research was to assess the efficiency of RS and GIS for predicting the flood risk in Raiganj Sub-division, Eastern India using the Frequency ratio, Entropy index, and Weight of evidence-information Value models. Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that factors such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. From the estimation, it is found that about 11.02 per cent (Frequency Ratio result), 13.90 per cent (Entropy’s result), and 11.50 per cent (WofE-IV results) of the total area has very high vulnerability status respectively. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The validation of the results indicates that the success rate of the presently constructed maps was 0.933 for the FR model, 0.917 for the SEI model, and 0.907 for the WofE model indicating that the frequency ratio model for mapping flood risk in the study region is more authentic, reliable, and useful for delineating flood vulnerable areas and potential flood risk sites. The findings of the analysis will help planners to develop flood prevention measures as part of regional flood risk management programs, as well as provide a foundation for future research in the study area.

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References

  • Adiat KAN, Nawawi MNM, Abdullah K (2012) Integration of geographic information system and 2D imaging to investigate the effects of subsurface conditions on flood occurrence. Mod App Sci 6(3):11–21

    Google Scholar 

  • Al-Hinai H, Abdalla R (2021) Mapping coastal flood susceptible areas using shannon’s entropy model: the case of muscat governorate. Oman ISPRS Int J Geoin 10(4):252

    Article  Google Scholar 

  • Al-Juaidi AE, Nassar AM, Al-Juaidi OE (2018) Evaluation of flood probability mapping using logistic regression and GIS conditioning factors. Arab J Geosci 11(24):1–10

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide probability mapping. Cat 114:21–36

    Google Scholar 

  • Aniya M (1985) Landslide-probability mapping in the Amahata river basin. Japan Ann Ass Amer Geo 75(1):102–114

    Article  Google Scholar 

  • Armaş I (2012) Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians Romania. Nat Haz 60(3):937–950

    Article  Google Scholar 

  • Avand M, Moradi H (2021a) Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed. Iran Adv Space Res 67(10):3169–3186

    Article  Google Scholar 

  • Avand M, Moradi H (2021b) Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. J Hydrolo 595:125663

    Article  Google Scholar 

  • Avand M, Khiavi AN, Khazaei M, Tiefenbacher JP (2021a) Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning. J Environ Manage 295:113040

    Article  Google Scholar 

  • Avand M, Moradi HR, Ramazanzadeh LM (2021b) Spatial prediction of future flood risk: an approach to the effects of climate change. Geosci 11(1):25

    Article  Google Scholar 

  • Azareh A, Rafiei SE, Choubin B, Barkhori S, Shahdadi A, Adamowski J, Shamshirband S (2019) Incorporating multi-criteria decision-making and fuzzy-value functions for flood probability assessment. Geocart Int. https://doi.org/10.1080/10106049.2019.1695958

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Comp Meth Geosci 13:398

    Google Scholar 

  • Central Water Commission (CWC), (2010) Water and related statistics water resource information system directorate. New Delhi 198–247

  • Chakraborty T, Kar R, Ghosh P, Basu S (2010) Kosimegafan: historical records, geomorphology and the recent avulsion of the Kosi River. Quat Int 227(2):143–160

    Article  Google Scholar 

  • Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood probability assessment. Environ Mod Soft 95:229–245

    Article  Google Scholar 

  • Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Haz 30:451–472

    Article  Google Scholar 

  • Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydro 375(3–4):613–626

    Article  Google Scholar 

  • Costache R, Pham QB, Sharifi E, Linh NTT, Abba SI, Vojtek M, Khoi DN (2020) Flash-flood probability assessment using multi-criteria decision making and machine learning supported by remote sensing and gis techniques. Rem Sens 12(1):106

    Article  Google Scholar 

  • Dano UL, Balogun AL, Matori AN, Wan YK, Abubakar IR, Said Mohamed MA, Pradhan B (2019) Flood probability mapping using GIS-based analytic network process: a case study of Perlis. Malaysia Wat 11(3):615

    Google Scholar 

  • Dhar ON, Nandargi S (2003) Hydro meteorological aspects of floods in India. Nat Haz 28(1):1–33

    Article  Google Scholar 

  • Dhar ON, Mandal BN, Ghose GC (1981a) Vamsadhara flash flood of September 1980 - a brief appraisal. Va Man 11:7–11

    Google Scholar 

  • Dhar ON, Rakhecha PR, Mandal BN, Sangam RB (1981b) The rainstorm which caused the Morvi dam disaster in August 1979/L’orage qui a provoqué la catastrophe du barrage Morviaoût 1979. Hydr Sci J 26(1):71–81

    Google Scholar 

  • Fernández DS, Lutz MA (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geo 111:90–98

    Article  Google Scholar 

  • Glenn E, Morino K, Nagler P, Murray R, Pearlstein S, Hultine K (2012) Roles of saltcedar (Tamarixspp) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J Ari EnviRon 79:56–65

    Article  Google Scholar 

  • Gül GO (2013) Estimating flood exposure potentials in Turkish catchments through index-based flood mapping. Nat Haz 69:403–423

    Article  Google Scholar 

  • Gupta S, Javed A, Dutt D (2003) Economics of flood protection in India. Nat Haz 28:199–210

    Article  Google Scholar 

  • Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Ear Sys Sci 126(3):39

    Article  Google Scholar 

  • Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu AX, Kazakis N (2018) Flood probability assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Tot Environ 621:1124–1141

    Article  CAS  Google Scholar 

  • Jebur MN, Pradhan B, Tehrany MS (2014) Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest, Cameron Highlands Malaysia. J Geo Nat Haz Ri 6(8):741–759

    Google Scholar 

  • Kafira V, Albanakis K, and Oikonomidis D (2014) Flood probability assessment using GIS An example from Kassandra Peninsula, Halkidiki, Greece. In: Proc 10th Int Congress Hel Geo Soci Thessaloniki, Greece 287–308

  • Kalsi SR, Srivastava KB (2006) Characteristic features of Orissa super cyclone of 29th October, 1999 as observed through CDR Paradip. Maus 57(1):21

    Article  Google Scholar 

  • Kanani-Sadat Y, Arabsheibani R, Karimipour F, Nasseri M (2019) A new approach to flood probability assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. J Hydro 572:17–31

    Article  Google Scholar 

  • Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. MeaSure 131:35–41

    Google Scholar 

  • Khoirunisa N, Ku CY, Liu CY (2021) A GIS-based artificial neural network model for flood probability assessment. Inter J Environ Res PubHea 18(3):1072

    Article  Google Scholar 

  • Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016a) A GIS-based flood probability assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Haz 83(2):947–987

    Article  Google Scholar 

  • Khosravi K, Pourghasemi HR, Chapi K, Bahri M (2016b) Flash flood probability analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ Moni Asses 188(12):1–21

    Google Scholar 

  • Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin. Malaysia Environ Earth Sci 67(1):251–264

    Article  Google Scholar 

  • Kourgialas NN, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas: a case study. Hydro Sci J 56:212–225

    Article  Google Scholar 

  • Lappas I, and Kallioras A (2019) Flood probability assessment through GIS-based multi-criteria approach and analytical hierarchy process (AHP) in a river basin in Central Greece. para (Malczewski, 1999):6(03)

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):3341. https://doi.org/10.1007/S10346-006-0047-Y

    Article  Google Scholar 

  • Lee S, Lee S, Lee MJ, Jung HS (2018) Spatial assessment of urban flood probability using data mining and geographic information System (GIS) tools. Sust 10(3):648

    Article  Google Scholar 

  • Lee MJ, Kang JE, and Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area probability mapping using GIS. Int geosci remote senssympo 895–898

  • Manandhar B (2010) Flood plain analysis and risk assessment of Lothar Khola, MSc Thesis, Tribhuvan University, Phokara, Nepal, 64

  • Manap AM, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF (2014) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Ara J Geosci 7(2):711–724. https://doi.org/10.1007/s12517-012-0795-z

    Article  Google Scholar 

  • Moghaddam DD, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2015) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed. Iran Arab J Geosci 8(2):913–929

    Article  Google Scholar 

  • Mondal S, Maiti R (2013) Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide probability mapping of Shiv-khola watershed, Darjeeling Himalaya. Int J Dis Ris Sci 4(4):200–212

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide probability mapping for shallow landslides in a tropical hilly area. Comp Geosci 37:1264–1276

    Article  Google Scholar 

  • Ouma YO, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Wat 6(6):1515–1545

    Article  Google Scholar 

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide probability mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197

    Article  Google Scholar 

  • Pham BT, Avand M, Janizadeh S, Phong TV, Al-Ansari N, Ho LS, Prakash I (2020) GIS based hybrid computational approaches for flash flood probability assessment. Wat 12(3):683

    Article  Google Scholar 

  • Pourghasemi HR, Mohammadi M, Pradhan B (2012) Landslide probability mapping using index of entropy and conditional probability models at Safarood Basin. Iran Cat 97:71–84

    Google Scholar 

  • Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province. Iran. Hydrogeo J 22(3):643–662. https://doi.org/10.1007/s10040-013-1089-6

    Article  Google Scholar 

  • Pradhan B (2010) Flood susceptible mapping and risk area estimation using logistic regression, GIS and remote sensing. J Spat Hydro 9(2):1–18

    Google Scholar 

  • O Rahmati, SA Nazari, M Mahdavi, HR Pourghasemi, H Zeinivand, (2014) Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab J Geosci https://doi.org/10.1007/s12517-014-1668-4

  • Rahmati O, Samani AN, Mahdavi M, PourghasemiHR ZH (2015) Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab J Geosci 8(9):7059–7071

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood probability mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int 31(1):42–70

    Article  Google Scholar 

  • Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide probability zonation in Greece. Nat Haz 65(1):523–543

    Article  Google Scholar 

  • Sachdeva S, Bhatia T, and Verma AK (2017) Flood probability mapping using GIS-based support vector machine and particle swarm optimization: A case study in Uttarakhand (India) In: 2017 8th International conference on computing, communication and networking technologies (ICCCNT), IEEE, (pp 1–7)

  • Saha A, Pal SC, Arabameri A, Blaschke T, Panahi S, ChowdhuriI AA (2021a) Flood probability assessment using novel ensemble of hyperpipes and support vector regression algorithms. Wat 13(2):241

    Article  Google Scholar 

  • Saha S, Sarkar D, Mondal P, Goswami S (2021b) GIS and multi-criteria decision-making assessment of sites suitability for agriculture in an anabranching site of sooin river. India Model Earth Syst Environ 7(1):571–588

    Article  Google Scholar 

  • Saha S, and Mondal P (2020) A catastrophic flooding event in North Bengal, 2017 and its impact assessment: A case study of Raiganj CD Block Uttar Dinajpur, West Bengal. AppliGeospat Tech Geomorpho Environ IGI Conf ISBN 978–81–925799–3–1

  • Sahana M, Patel PP (2019) A comparison of frequency ratio and fuzzy logic models for flood probability assessment of the lower Kosi River Basin in India. Environ Earth Sci 78(10):1–27

    Article  Google Scholar 

  • Sahana M, Rehman S, Sajjad H, Hong H (2020) Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood probability assessment: a study of Sundarban Biosphere Reserve. India. Cat 189:104450

    Google Scholar 

  • Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR (2018a) Flood probability mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin India. Model Earth Syst Environ 4(1):395–408

    Article  Google Scholar 

  • Samanta S, Pal DK, Palsamanta B (2018b) Flood probability analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8(2):1–14

    Article  Google Scholar 

  • Sarkar D, Saha S, and Mondal P (2021) GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. Int J Environ Sci Technol 1–22

  • Sarkar D, Mondal P (2020) Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin. Indo-Bangladesh Barind Region Appl Water Sci 10(1):1–13

    CAS  Google Scholar 

  • Shafapour Tehrany M, Kumar L, Neamah Jebur M, Shabani F (2019) Evaluating the application of the statistical index method in flood probability mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Haz Risk 10(1):79–101

    Article  Google Scholar 

  • Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile Comput Comm Rev 5(1):3–55

    Article  Google Scholar 

  • Singh O, Kumar M (2013) Flood events, fatalities and damages in India from 1978 to 2006. Nat Hazards 69(3):1815–1834

    Article  Google Scholar 

  • Srdevic Z, Blagojevic B, Srdevic B (2011) AHP based group decision making in ranking loan applicants for purchasing irrigation equipment: a case study Bulgarian. J Agric Sci 17(4):531–543

    Google Scholar 

  • Tang Z, Yi S, Wang C, Xiao Y (2018) Incorporating probabilistic approach into local multi-criteria decision analysis for flood probability assessment. Stoch Environ Res Risk Assess 32(3):701–714

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2014) Flood probability mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydro 512:332–343

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood probability assessment using GIS-based support vector machine model with different kernel types. Cat 125:91–101

    Google Scholar 

  • Triantaphyllou E, Mann SH (1995) Using the analytic hierarchy process for decision making in engineering applications: some challenges. Int J Ind Eng: Theory Appl Pract 2(1):35–44

    Google Scholar 

  • Weier J, Herring D (2000) Measuring Vegetation (NDVIEVI) NASA Earth Observatory Washington. DC, USA

    Google Scholar 

  • Wilson JP, Gallant JC (2000) Terrain analysis: Principles and applications. Wiley, New York

    Google Scholar 

  • Yariyan P, Avand M, Abbaspour RA, Karami M, Tiefenbacher JP (2020a) GIS-based spatial modeling of snow avalanches using four novel ensemble models. Sci Total Environ 745:141008

    Article  CAS  Google Scholar 

  • Yariyan P, Avand M, Abbaspour RA, Torabi HA, Costache R, Ghorbanzadeh O, Blaschke T (2020b) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat, Nat Haz Risk 11(1):2282–2314

    Article  Google Scholar 

  • Yariyan P, Avand M, Omidvar E, Pham QB, Linh NTT, Tiefenbacher JP (2020c) Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto Int. https://doi.org/10.1080/10106049.2020.1870164

    Article  Google Scholar 

  • Yousefi S, Avand M, Yariyan P, Pourghasemi HR, Keesstra S, Tavangar S, Tabibian S (2020) A novel GIS-based ensemble technique for rangeland downward trend mapping as an ecological indicator change. Ecol Indic 117:106591

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Sefry SA (2016) Flash flood probability assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci 75(1):12

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to acknowledge all the agencies especially, the Indian Meteorological Department (IMD), Geological Survey of India, Survey of India (SOI), USGS, and Alaska Sattelite Facility as sources of data required for the study. We are also grateful to the reviewers, Editors for their insightful comments and suggestions for the improvement and expansion of the work. We'd like to thank Dr. Gopal Chandra Debnath (Retired Professor of Visva Bharati University, W.B.) and Dr. Narayan Chandra Ghosh (Former Professor of Rabindra Bharati University, W.B.) for their assistance with the data analysis and model validation parts.

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  1. Department of Geography, Raiganj University, Raiganj, West Bengal, 733134, India

    Sunil Saha, Debabrata Sarkar & Prolay Mondal

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  1. Sunil Saha
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Saha, S., Sarkar, D. & Mondal, P. Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India. Stoch Environ Res Risk Assess 36, 1721–1742 (2022). https://doi.org/10.1007/s00477-021-02115-9

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