Abstract
In recent years, floods have acquired global importance due to their devastating nature that can cause massive damage to infrastructure and society. The Himalayan foothill is very susceptible to flood since time immemorial, and therefore, the present study tries to assess the flood risk of the sub-Himalayan Jalpaiguri region using a multi-criteria decision approach. Hitherto, a detailed assessment of flood in the Himalayan foothill region is not carried due to a comprehensive database limitation. However, for the first time, a multi-source data of about seventeen parameters including, flood conditioning factors (viz. altitude, distance from rivers, slope, drainage density, geomorphology, flow accumulation, rainfall, topographic wetness index, geology, and soil) and socio-economic and infrastructural indicators (viz. population density, household density, Landcover, road distance, proximity to flood shelter, proximity to hospital and literacy) were used to prepare the flood susceptibility, vulnerability, and flood risk map for the study area. Furthermore, an administrative-wise microlevel risk assessment was also carried out in the present study. The result indicates that about 38% of the area is susceptible to high and very high flood zones, while about 58% of the area is covered under high to very high vulnerability zone. In the final flood risk map, about 29% of the area is under a high threat level that seeks immediate consideration. Furthermore, the reliability of this work can be assessed by validating the model using AUC, which gives an accuracy of 0.862 or 86.2%. Thus, the overall approach of this study can be applied for mitigation strategy and to prepare a policy framework to alleviate future flood incidences.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adhikari P, Hong Y, Douglas KR, Kirschbaum DB, Gourley J, Adler R, Brakenridge GR (2010) A digitized global flood inventory (1998–2008): compilation and preliminary results. Nat Hazards 55(2):405–422. https://doi.org/10.1007/s11069-010-9537-2
Ali KR, Ahmad A, Ahmad SN (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Model Earth Syst Environ 5:1083–1102. https://doi.org/10.1007/s40808-019-00593-z
Arabameri A, Rezaei K, Cerda A, Conoscenti C, Kalantari Z (2019) A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Sci Total Environ 660:443–458. https://doi.org/10.1016/j.scitotenv.2019.01.021
Arefin R (2020) Groundwater potential zone identification at Plio-Pleistocene elevated tract, Bangladesh: AHP-GIS and remote sensing approach. Groundw Sustain Dev 10:100340. https://doi.org/10.1016/j.gsd.2020.100340
Arya AK, Singh AP (2021) Multi criteria analysis for flood hazard mapping using GIS techniques: a case study of Ghaghara River basin in Uttar Pradesh, India. Arab J Geosci 14:656. https://doi.org/10.1007/s12517-021-06971-1
Ajin RS, Krishnamurthy RR, Jayaprakash M, Vinod PG (2013) Flood hazard assessment of Vamanapuram River basin, Kerala, India: an approach using remote sensing & GIS techniques. Adv Appl Sci Res 4(3):263–274
Benjmel K, Amraoui F, Boutaleb S (2020) Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco). Water. https://doi.org/10.3390/w12020471
Balica SF, Wrightm NG, Meulen FV (2012) A flood vulnerability index for coastal cities and its use inassessing climate change impacts. Nat Hazards 64:73–105. https://doi.org/10.1007/s11069-012-0234-1
Bui DT, Panahi M, Shahabi H, Singh VP, Shirzadi A, Chapi K, Khosravi K, Chen W, Panahi S, Li S, Bin AB (2018) Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods. Sci Rep 8:15364. https://doi.org/10.1038/s41598-018-33755-7
Bui DT, Pradhan B, Nampak H, Bui QT, Tran QA, Nguyen QP (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. J Hydrol 540:317–330. https://doi.org/10.1016/j.jhydrol.2016.06.027
Bui DT, Khosravi K, Shahabi H, Daggupati P, Adamowski JF, Melesse AM, Thai Pham B, Pourghasemi HR, Mahmoudi M, Bahrami S, Pradhan B, Shirzadi A, Chapi K, Lee S (2019) 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. Remote Sens 11(13):1589. https://doi.org/10.3390/rs11131589
Cardona O (2005) Indicators of disaster risk and risk management: Summary report. Inter-American Development Bank. https://www.ipcc.ch/apps/njlite/srex/njlite_download.php?id=6132
Census of India (2011) District Census Handbook, Jalpaiguri. Retriverd from https://censusindia.gov.in/2011census/dchb/DCHB_A/19/1902_PART_A_DCHB_JALPAIGURI.pdf
Chakraborty S, Datta K (2013) Causes and Consequences of Channel Changes – A Spatio-Temporal Analysis using Remote Sensing and Gis— Jaldhaka-Diana River System (Lower Course), Jalpaiguri (Duars), West Bengal, India. J Geogr Nat Disast 3:107. https://doi.org/10.4172/2167-0587.1000107
Chakraborty S (2017) Dynamics of hydro-geomorphological environment in Jaldhaka river basin of West Bengal and its impacts on landuse. Unpublished Ph.D. thesis, Visva-Bharati, Santiniketan. http://hdl.handle.net/10603/227699
Chakraborty S, Mukhopadhyay S (2019) Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Nat Hazards 99(1):247–274. https://doi.org/10.1007/s11069-019-03737-7
Chen W, Cutter SL, Emrich CT, Shi P (2013) Measuring social vulnerability to natural hazards in the Yangtze River Delta region, China. Int J Disaster Risk Sci 4(4):169–181. https://doi.org/10.1007/s13753-013-0018-6
Chen W, Li Y, Xue W, Shahabi H, Shaojun LH, Hong X, Wang H, Bian S, Zhang B, Pradhan B, Ahmad (2019) Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Sci Total Environ 701:134979. https://doi.org/10.1016/j.scitotenv.2019.134979
Danumah JH, Odai SN, Saley BM, Szarzynski J, Thiel M, Kwaku A, Kouame FK, Akpa LY (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (coted’ivoire). Geoenviron Disasters 3:10. https://doi.org/10.1186/s40677-016-0044-y
Dano UL, Balogun AL, Matori AN, Wan Yusouf K, Abubakar IR, Said Mohamed MA, Aina YA, Pradhan B (2019) Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia. Water 11(3):615. https://doi.org/10.3390/w11030615
Darabi H, Choubin B, Rahmati O, Haghighi AT, Pradhan B, Kløve B (2018) Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. J Hydrol 569:142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002
Das M, Chattopadhyay A, Basu R (2017) Spatial Flood Potential Mapping (SFPM) with Flood Probability and Exposure Indicators of Flood Vulnerability: Case Study Former Jalpaiguri District, West Bengal, India. J Geogr Nat Disast 7:210. https://doi.org/10.4172/2167-0587.1000210
Das S (2018) Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arab J Geosci 11:576. https://doi.org/10.1007/s12517-018-3933-4
Das S (2019) Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl: Society and Environment 14:60–74. https://doi.org/10.1016/j.rsase.2019.02.006
Das S (2020) Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Remote Sensing Appl: Society and Environment 20:100379. https://doi.org/10.1016/j.rsase.2020.100379
de Brito MM, Evers M (2016) Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat Hazards Earth Syst Sci 16(4):1019–1033. https://doi.org/10.5194/nhess-16-1019-2016
Dhar ON, Nandargi S (2003) Hydrometeorological aspects of floods in India. Nat Hazards 28(1):1–33. https://doi.org/10.1023/A:1021199714487
District Census Handbook, Jalpaiguri (2011) Census of India 2011. WEST BENGAL. Retrieved from:https://censusindia.gov.in/2011census/dchb/DCHB_A/19/1902_PART_A_DCHB_JALPAIGURI.pdf
El-Haddad BA, Youssef AM, Pourghasemi HR, Pradhan B, El-Shater AH, El-Khashab MH (2020) Flood susceptibility prediction using four machine learning techniques and comparison of their performance at WadiQena Basin, Egypt. Nat Hazards 105(1):83–114. https://doi.org/10.1007/s11069-020-04296-y
El-Magd SAA, Pradhan B, Alamri A (2021) Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt. Arab J Geosci 14:323. https://doi.org/10.1007/s12517-021-06466-z
Elkhrachy I (2015) Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). Egypt J Remote Sens Space Sci 18(2):261–278. https://doi.org/10.1016/j.ejrs.2015.06.007
Ghosh A (2016) Quantitative approach on erosion hazard, vulnerability and risk assessment: case study of Muriganga–Saptamukhi interfluve. Nat Hazards 87:1709–1729. https://doi.org/10.1007/s11069-017-2844-0
Ghosh A, Kar SK (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Nat Hazards 94(1):349–368. https://doi.org/10.1007/s11069-018-3392-y
Ghosh A, Mandal M, Banerjee M, Karmakar M (2020) Impact of hydro-geological environment on availability of groundwater using analytical hierarchy process (AHP) and geospatial techniques: A study from the upper Kangsabati river basin. Groundw Sustain Dev 11:100419. https://doi.org/10.1016/j.gsd.2020.100419
Ghosh M, Ghosal S (2020) Climate change vulnerability of rural households in flood-prone areas of Himalayan foothills, West Bengal, India. Environ Dev Sustain 23:2570–2595. https://doi.org/10.1007/s10668-020-00687-0
Gupta S, Javed A, Datt D (2003) Economics of flood protection in India. In: In Flood Problem and Management in South Asia. Springer, Dordrecht, pp 199–210. https://doi.org/10.1007/978-94-017-0137-2_10
Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126(3):39
Hammami S, Zouhri L, Souissi D, Souei A, Zghibi Z, Marzougui A, Dlala M (2019) Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arab J Geosci 12:653. https://doi.org/10.1007/s12517-019-4754-9
Handfield R, Walton SV, Sroufe R, Melnyk SA (2002) Applying environmental criteria to supplier assessment: a study in the application of the analytical hierarchy process. Eur J Oper Res 141:70–87. https://doi.org/10.1016/S0377-2217(01)00261-2
Hazarika N, Barman D, Das AK, Sarma AK, Borah SB (2018) Assessing and mapping flood hazard, vulnerability and risk in the Upper Brahmaputra River valley using stakeholders’ knowledge and multicriteria evaluation (MCE). J Flood Risk Manag 11(52):S700–S716. https://doi.org/10.1111/jfr3.12237
Hindustan times (2016) Bengal floods: 58,000 people affected, red alert issued in Jalpaiguri. Retrieved from:https://www.hindustantimes.com/india-news/bengal-floods-58-000-people-affected-red-alert-issued-in-jalpaiguri/story-V9c7a9NRZa1kNiuAJcC5kO.html
Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019
Horton RE (1945) Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56(3):275–370. https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2
Hoque MAA, Tasfia S, Ahmed N, Pradhan B (2019) Assessing spatial flood vulnerability at KalaparaUpazila in Bangladesh using an analytic hierarchy process. Sensors 19(6):1302. https://doi.org/10.3390/s19061302
Hu S, Cheng X, Zhou D, Zhang H (2017) GIS-based flood risk assessment in suburban areas: a case study of the Fangshan District, Beijing. Nat Hazards 87:1525–1543. https://doi.org/10.1007/s11069-017-2828-0
Ibrahim-Bathis K, Ahmed SA (2016) Geospatial technology for delineating groundwater potential zones in Doddahalla watershed of Chitradurga district. India Egypt J Remote Sens Space Sci 19(2):223–234. https://doi.org/10.1016/j.ejrs.2016.06.002
Kale VS (2004) Floods in India: their frequency and pattern. Coping with natural hazards: Indian context. Orient Longman, Hyderabad, pp 91–103
Kandilioti G, Makropoulos C (2012) Preliminary flood risk assessment: the case of Athens. Nat Hazards 61:441–468. https://doi.org/10.1007/s11069-011-9930-5
Kaur H, Gupta S, Parkash S, Thapa R, Mandal R (2017) Geospatial modelling of flood susceptibility pattern in a subtropical area of West Bengal, India. Environ Earth Sci 76(9):339
Kazakis N, Kougias I, Patsialis T (2015) Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope–Evros region, Greece. Sci of Total Environ 538:555–563. https://doi.org/10.1016/j.scitotenv.2015.08.055
Khosravi K, Panahi M, Tien Bui D (2018) Spatial prediction of groundwater spring potential mapping based on adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrol Earth Syst Sci 22:4771–4792. https://doi.org/10.5194/hess-22-4771-2018
Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Gróf G, Ho HL, Hong H, Chapi K, Prakash I (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073
Kittipongvises S, Phetrak A, Rattanapun P, Brundiers K, Buizer JL, Melnick R (2020) AHP-GIS analysis for flood hazard assessment of the communities nearby the world heritage site on Ayutthaya Island, Thailand. Int J Disaster Risk Reduct 48:101612. https://doi.org/10.1016/j.ijdrr.2020.101612
Komolafe AA, Herath S, Avtar R (2018) Methodology to assess potential flood damages in urban areas under the influence of climate change. Nat Hazards Review 19(2):05018001. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000278
Kim S, Lee W, Shin K, Kafatos M, Seo D, Kwak H (2011) Comparison of spatial interpolation techniques for predicting climate factors in Korea. For Sci Technol 6:97–109. https://doi.org/10.1080/21580103.2010.9671977
Kourgialas NN, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas—a case study. Hydrol Sci J–Jl des Sci Hydrol 56(2):212–225. https://doi.org/10.1080/02626667.2011.555836
Kron W, Steuer M, Löw P, Wirtz A (2012) How to deal properly with a natural catastrophe database–analysis of flood losses. Nat Hazards Earth Syst Sci 12(3):535–550. https://doi.org/10.5194/nhess-12-535-2012
Ly S, Charles C, Degré A (2013) Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale. A review. Biotechnol Agron Soc Environ 17(2):392–406
Mahalanobis PC (1927) Rainfall and floods in North Bengal. Bengal SecretariatPress, Kolkata, pp 1870–1922
Malik S, Chandra Pal S, Chowdhuri I, Chakrabortty R, Roy P, Das B (2020) Prediction of Highly Flood Prone Areas by GIS Based Heuristic and Statistical Model in a Monsoon Dominated Region of Bengal Basin. Remote Sens Appl Soc Environ 19:100343. https://doi.org/10.1016/j.rsase.2020.100343
Mandal S, Sarkar S (2016) Overprint of neotectonism along the course of River Chel, North Bengal, India. J Palaeogeogr 5(3):221–240. https://doi.org/10.1016/j.jop.2016.05.004
Merlotto A, Bertola GR, Piccolo MC (2016) Hazard, vulnerability and coastal erosion risk assessment in Necochea Municipality, Buenos Aires Province, Argentina. J Coast Conserv 20:351–362. https://doi.org/10.1007/s11852-016-0447-7
Mirzaei S, Vafakhah M, Pradhan B, Alavi SJ (2020) Flood susceptibility assessment using extreme gradient boosting (EGB), Iran. Earth Sci Inf 14(1):51–67. https://doi.org/10.1007/s12145-020-00530-0
Mishra K, Sinha R (2020) Flood risk assessment in the Kosimegafan using multi-criteria decision analysis: A hydro-geomorphic approach. Geomorphology 350:106861. https://doi.org/10.1016/j.geomorph.2019.106861
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30. https://doi.org/10.1002/hyp.3360050103
Muralitharan J, Palanivel K (2015) Groundwater targeting using remote sensing, geographical information system and analytical hierarchy process method in hard rock aquifer system, Karur district, Tamil Nadu, India. Earth Sci Inf 8(4):827–842. https://doi.org/10.1007/s12145-015-0213-7
Nied M, Schröter K, Lüdtke S, Nguyen VD, Merz B (2017) What are the hydro-meteorological controls on flood characteristics? J Hydrol 545:310–326. https://doi.org/10.1016/j.jhydrol.2016.12.003
Ngo PTT, Hoang ND, Pradhan B, Nguyen QK, Tran XT, Nguyen QM, Nguyen VN, Samui P, Bui DT (2018) A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data. Sensors 18(11):3704. https://doi.org/10.3390/s18113704
Ogden FL, Raj Pradhan N, Downer CW, Zahner JA (2011) Relative importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment. Water Resour Res 47(12). https://doi.org/10.1029/2011WR010550
Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365. https://doi.org/10.1007/s12517-012-0532-7
Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spatial Hydrol 9:1–18
Prokop P, Walanus A (2017) Impact of the Darjeeling–Bhutan Himalayan front on rainfall hazard pattern. Nat Hazards 89:387–404. https://doi.org/10.1007/s11069-017-2970-8
Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int 31:42–70. https://doi.org/10.1080/10106049.2015.1041559
Reneau SL (2000) Stream incision and terrace development in Frijoles Canyon,Bandelier National Monument, New Mexico, and the influence of lithology and climate. Geomorphology 32:171–193. https://doi.org/10.1016/S0169-555X(99)00094-X
Rimba AB, Setiawati MD, Sambah AB, Miura F (2017) Physical Flood Vulnerability Mapping Applying Geospatial Techniques in Okazaki City, Aichi Prefecture. Japan Urban Sci 1:7. https://doi.org/10.3390/urbansci1010007
Rozalis S, Morin E, Yair Y, Price C (2010) Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions. J Hydrol 394:245–255. https://doi.org/10.1016/j.jhydrol.2010.03.021
Roy S (2011) Flood Hazards in Jalpaiguri District. Unpublished Ph.D. Thesis, Department of Applied Geography, University of North Bengal, Siliguri. http://ir.nbu.ac.in/handle/123456789/1335
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International Book Co, New York, London ISBN: 0070543712 9780070543713
Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26. https://doi.org/10.1016/0377-2217(90)90057-I
Saha AK, Agrawal S (2020) Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnol Environ Eng 5:11. https://doi.org/10.1007/s41204-020-00073-1
Samanta S, Koloa C, Kumar Pal D, Palsamanta B (2016) Flood risk analysis in lower part of Markham river based on multi-criteria decision approach (MCDA). Hydrology 3(3):29. https://doi.org/10.3390/hydrology3030029
Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8:66. https://doi.org/10.1007/s13201-018-0710-1
Sener S, Sener E, Karagüzel R (2011) Solid waste disposal site selection with GIS and AHP methodology: a case study in Senirkent-Uluborlu (Isparta) Basin, Turkey. Environ Monit Assess 173(1–4):533–554. https://doi.org/10.1007/s10661-010-1403-x
Sharma S, Roy PS, Chakravarthi V (2017) Flood risk assessment using multicriteria analysis: a case study from Kopili River Basin, Assam, India. Geomatics Nat Hazards Risk 9:79–93. https://doi.org/10.1080/19475705.2017.1408705
Shrestha UB, Gautam S, Bawa KS (2012) Widespread climate change in the Himalayas and associated changes in local ecosystems. PloS 7(5):1–10. https://doi.org/10.1371/journal.pone.0036741
Shrestha A, Agrawal N, Alfthan B, Bajracharya SR, Maréchal J, Oort BV (2015) The Himalayan climate and Water Atlas. ICIMOD, Kathmandu Retrieved from: https://lib.icimod.org/record/31180
Smith K, Ward R (1998) Mitigating and managing flood losses. Floods: Physical Processes and Human Impacts. John Wiley & Sons, Chichester ISBN: 978-0-471-95248-0
Starkel L, Sarkar S, Soja R, Prokop P (2008) Present-day evolution of the Sikkimese-Bhutanese Himalayan piedmont. IGiPZ PAN, 219. https://www.rcin.org.pl/igipz/publication/225
Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2019) GIS based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int 35:991–1017. https://doi.org/10.1080/10106049.2019.1566405
Tang X, Li J, Liu M, Liu W, Hong H (2020) Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods. Catena 190:104536. https://doi.org/10.1016/j.catena.2020.104536
Tapsell SM, Penning-Rowsell EC, Tunstall SM, Wilson TL (2002) Vulnerability to flooding: health and social dimensions. Phil Trans R Soc A 360:1511–1525. https://doi.org/10.1098/rsta.2002.1013
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. https://doi.org/10.1016/j.jhydrol.2013.09.034
Tehrany MS, Lee MJ, Pradhan B, Jebur MN, Lee S (2014) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72:4001–4015. https://doi.org/10.1007/s12665-014-3289-3
Tehrany MS, Pradhan B, Jebur MN (2015a) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Env Res Risk A 29(4):1149–1165. https://doi.org/10.1007/s00477-015-1021-9
Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015b) Flood susceptibility assessment using GISbased support vector machine model with different kernel types. Catena 125:91–101. https://doi.org/10.1016/j.catena.2014.10.017
Telegraph (2020) Rains trigger floods in three districts. Retrieved from:https://www.telegraphindia.com/west-bengal/rains-trigger-floods-in-jalpaiguri-alipurduar-and-cooch-behar-districts/cid/1783775.
The economic times (2015) 400 people affected by flood in West Bengal's Jalpaiguri district. Retrieved from: https://economictimes.indiatimes.com/news/politics-and-nation/400-people-affected-by-flood-in-west-bengals-jalpaiguri-district/articleshow/47893460.cms.
Vishnu CL, Sajinkumar KS, Oommen T, Coffman RA, Thrivikramji KP, Rani VR, Keerthy RS (2019) Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomatics Nat Hazards Risk 10:758–767. https://doi.org/10.1080/19475705.2018.1543212
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274–287. https://doi.org/10.1016/j.catena.2011.01.014
Youssef AM, Pradhan B, Sefry SA (2016) Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci 75(1):12. https://doi.org/10.1007/s12665-015-4830-8
Zhao G, Pang B, Xu Z, Yue J, Tu T (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ 615:1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037
Acknowledgements
The author wishes thanks to the Department of Geography and Applied Geography, University of North Bengal, for providing the necessary facilities to conduct this research. Also, the authors would like to acknowledge Mr. Gopal Das, Assistant teacher of Geography, Maynaguri Subhas Nagar High School, for his support during the field investigation, without which it is not possible to find such novel results. Besides, the authors would like to express their sincere gratitude to the Editor-in-chief Abdullah Al-Amri and Prof. Biswajeet Pradhan for their insightful suggestions and comments, which immensely helped in the improvement of the earlier version of the manuscript. Subsequently, the constructive comments from two anonymous reviewers significantly improved the quality of the manuscript. Lastly, the author would like to thank Mr. Nimai Singha and Mr. Debanjan Basak, research scholar of CBPBU and NBU for their support throughout the study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
This research study is carried out in compliance with field knowledge, transparency, moral values and hard work.
Ethics approval
Not applicable
Consent to participate
All the co-authors read this article before submission and every step are informed to all the co-authors.
Conflict of Interest
The authors declare that they have no competing interests.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Roy, S., Bose, A. & Chowdhury, I.R. Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India. Arab J Geosci 14, 999 (2021). https://doi.org/10.1007/s12517-021-07324-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-07324-8