Abstract
Among all-natural disasters, flood is the most common and devastating, causing extensive disruption to the environment, socio-economy, infrastructure, and many other aspects of human life. Almost every year, the Torsa- Raidak River integrated basin area of the Himalayan foothill experiences flood due to physiographic characteristics and excessive rainfall over a short period of time. The current study uses Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) model to prepare a flood susceptibility map. According to their contributions of selected factors (elevation, rainfall, topographic wetness index, slope angle, distance from rivers, and land use land cover), weightage was given using the AHP method. Moreover, AHP and FR methods were employed to find out the flood vulnerability index (FVI). Current research results revealed that the lower part of the basin (Alipurduar and Cooch Behar) is susceptible to high to very high flood risk. Rainfall, LULC and distance from the river are contributing the most to cause flood in this study area. A total of 156 flood points were selected from different historical flood maps and field study areas for validation. The output of validation based on ROC depicted that the prediction accuracy was 81.2%, 85.7%, and 86.6% for the FVI, FR, and AHP, respectively, which may consider the model as good and acceptable for floods prediction. This research is capable to act as a guideline for grounding flood control measures in the area of study.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali SA, Khatun R, 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
Central Water Commission (CWC) (2010) Water and related statistics, water resource information system directorate, New Delhi. Central Water Commission (CWC), New Delhi
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, Li Y, Xue W, Shahabi H, Li S, Hong H, Wang X, Bian H, Zhang S, Pradhan B, Ahmad BB (2020) Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Sci Total Environ 701:134979
Chowdhuri I, Pal SC, Chakrabortty R (2020) Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res 65:1466–1489. https://doi.org/10.1016/j.asr.2019.12.003
Dano U, Balogun A-L, Matori A-N et al (2019) Flood susceptibility mapping using GIS-based analytic network process: a case study of Perlis Malaysia. Water 11:615. https://doi.org/10.3390/w11030615
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 Soc Environ 14:60–74. https://doi.org/10.1016/j.rsase.2019.02.006
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 18:261–278. https://doi.org/10.1016/j.ejrs.2015.06.007
Feloni E, Mousadis I, Baltas E (2020) Flood vulnerability assessment using a GIS‐based multi‐criteria approach—the case of Attica region. J Flood Risk Manag 13. https://doi.org/10.1111/jfr3.12563
Gazi MY, Islam MA, Hossain S (2019) Flood-hazard mapping in a regional scale—way forward to the future hazard Atlas in Bangladesh. Malays J Geosci (MJG) 3:1–11
Gupta S, Javed A, Datt D (2003) Economics of flood protection in India. Nat Hazards 28:199–210. https://doi.org/10.1023/A:1021142404009
Hammami S, Zouhri L, Souissi D et al (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
Hoque M, Tasfia S, Ahmed N, Pradhan B (2019) Assessing spatial flood vulnerability at KalaparaUpazila in Bangladesh using an analytic hierarchy process. Sensors 19:1302. https://doi.org/10.3390/s19061302
Hasanuzzaman M, Mandal S (2020) A morphology-independent methodology to assess erosion, accretion and lateral migration of an alluvial channel using geospatial tools: a study on the Raidak-I river of Himalayan foothills. Sustain Water Resour Manag 6:35. https://doi.org/10.1007/s40899-020-00393-9
Hasanuzzaman M, Gayen A, Shit P (2021) Channel dynamics and geomorphological adjustments of Kaljani River in Himalayan foothills. Geocarto Int 1–28. https://doi.org/10.1080/10106049.2021.1882008
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
Jabbar FK, Grote K, Tucker RE (2019) A novel approach for assessing watershed susceptibility using weighted overlay and analytical hierarchy process (AHP) methodology: a case study in Eagle Creek Watershed, USA. Environ Sci Pollut Res 26:31981–31997. https://doi.org/10.1007/s11356-019-06355-9
Jana MM (1997) Management and development of River Basins in North Bengal using remote sensing techniques. J Indian Soc Remote Sens 25(2):105–111
Janizadeh S, Avand M, Jaafari A et al (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh Watershe Iran. Sustainability 11:5426. https://doi.org/10.3390/su11195426
Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility 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 Hazards 83:947–987. https://doi.org/10.1007/s11069-016-2357-2
Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly H-B, Gr´of G, Ho H.L, 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
Lawal DU (2012) Detecting flood susceptible areas using GIS-based analytic hierarchy process. 2012 International conference on future environment and energy. IACSIT press, Singapoore, pp 1–5
Leskens JG, Brugnach M, Hoekstra AY, Schuurmans W (2014) Why are decisions in flood disaster management so poorly supported by information from flood models? Environ Model Softw 53(53):61. https://doi.org/10.1016/j.envsoft.2013.11.003
Lee S, Lee S, Lee M-J, Jung H-S (2018) Spatial assessment of urban flood susceptibility using data mining and geographic information system (GIS) tools. Sustainability 10:648. https://doi.org/10.3390/su10030648
Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990
Lee MJ, Kang JE, Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS, pp 895–898. Geoscience and Remote Sensing Symposium (IGARSS), Munich
Liuzzo L, Sammartano V, Freni G (2019) Comparison between different distributed methods for flood susceptibility mapping. Water Resour Manage 33:3155–3173. https://doi.org/10.1007/s11269-019-02293-w
Matori AN, Lawal DU, Yusof KW, et al (2014) Spatial analytic hierarchy process model for flood forecasting: an integrated approach. IOP Conf Ser Earth Environ Sci 20:012029. https://doi.org/10.1088/1755-1315/20/1/012029
Majumder R, Bhunia GS, Patra P, Mandal AC, Ghosh D, Shit PK (2019) Assessment of flood hotspot at a village level using GIS-based spatial statistical techniques. Arab J Geosci 12(13):1–12. https://doi.org/10.1007/s12517-019-4558-y
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
Nath SK, Roy D, Singh Thingbaijam KK (2008) Disaster mitigation and management for West Bengal, India—an appraisal. Curr Sci 94(7):858–866
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
Periyasamy P, Yagoub MM, Sudalaimuthu M (2018) Flood vulnerable zones in the rural blocks of Thiruvallur district South India. Geoenviron Disasters 5:21. https://doi.org/10.1186/s40677-018-0113-5
Phrakonkham S, Kazama S, Komori D, Sopha S (2019) Distributed hydrological model for assessing flood hazards in Laos. JWARP 11:937–958. https://doi.org/10.4236/jwarp.2019.118056
Pradhan B (2010) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2):1–18
Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2013) A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomat Nat Hazards Risk 4:93–118
Rahman M, Ningsheng C, Islam MM et al (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ 3:585–601. https://doi.org/10.1007/s41748-019-00123-y
Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Provinc Iran. Geocarto Int 31:42–70. https://doi.org/10.1080/10106049.2015.1041559
Roslee R, Tongkul F, Mariappan S, Simon N (2018) Flood hazard analysis (FHAn) using multi-criteria evaluation (MCE) in Penampang Area, Sabah Malaysia. ASM Sci J 11(3):104–122
Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International Book Co., New York, London
Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26
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 10:17. https://doi.org/10.1007/s13201-019-1102-x
Sahana M, Rehman S, Sajjad H, Hong H (2020) Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: a study of Sundarban Biosphere Reserve, India. Catena 189:104450
Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR (2018) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4:395–408. https://doi.org/10.1007/s40808-018-0427-z
Saha TK, Pal S (2019) Emerging conflict between agriculture extension and physical existence of wetland in post-dam period in Atreyee River Basin of Indo-Bangladesh. Environ Dev Sustain 21(3):1485–1505
Şen Z (2018) Flood modelling, predication and mitigation. Springer International Publishing, Cham
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
Singh O, Kumar M (2017) Flood occurrences, damages, and management challenges in India: a geographical perspective. Arab J Geosci 10:102. https://doi.org/10.1007/s12517-017-2895-2
Souissi D, Msaddek MM, Zouhri L, Chenini I, El May M, Dlala M (2018) Mapping groundwater recharge potential zones in arid region using GIS and Landsat approaches, SE Tunisia. Hydrol Sci J. https://doi.org/10.1080/02626667.2017.1414383
Sørensen R, Zinko U, Seibert J (2006) On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol Earth Syst Sci 10:101–112. https://doi.org/10.5194/hess-10-101-2006
Subbarayan S, Sivaranjani S (2020) Modelling of flood susceptibility based on GIS and analytical hierarchy process—A Case study of Adayar River Basin, Tamilnadu, India. In: Pal I, von Meding J, Shrestha S, Ahmed I, Gajendran T (eds) An Interdisciplinary Approach for Disaster Resilience and Sustainability, pp 91–110. Springer, Singapore
Tehrany MS, Jones S (2017) Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extent of the flood inventory. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-4/W5:209–214. https://doi.org/10.5194/isprs-archives-XLII-4-W5-209-2017
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
Tehrany MS, Kumar L, NeamahJebur M, Shabani F (2019) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Haz Risk 10:79–101. https://doi.org/10.1080/19475705.2018.1506509
Tien Bui D, Khosravi K, Shahabi H et al (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 11:1589. https://doi.org/10.3390/rs11131589
Toduse NC, Ungurean C, Davidescu S, Clinciu I, Marin M, Nita MD, Davidescu A (2020) Torrential flood risk assessment and environmentally friendly solutions for small catchments located in the Romania Natura 2000 sites Ciucas, Postavaru and Mare. Sci Total Environ 698:134271. https://doi.org/10.1016/j.scitotenv.2019.134271
Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11:364. https://doi.org/10.3390/w11020364
Yousefi S, Mirzaee S, Keesstra S, Surian N, Pourghasemi HR, Zakizadeh HR, Tabibian S (2018) Effects of an extreme flood on river morphology (case study: Karoon River, Iran). Geomorphology 304:30–39
Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at vaz watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (mlp) and radial basic function (rbf) algorithms. Arab J Geosci 6:2873–2888
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hasanuzzaman, M., Adhikary, P.P., Bera, B., Shit, P.K. (2022). Flood Vulnerability Assessment Using AHP and Frequency Ratio Techniques. In: Pradhan, B., Shit, P.K., Bhunia, G.S., Adhikary, P.P., Pourghasemi, H.R. (eds) Spatial Modelling of Flood Risk and Flood Hazards. GIScience and Geo-environmental Modelling. Springer, Cham. https://doi.org/10.1007/978-3-030-94544-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-94544-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94543-5
Online ISBN: 978-3-030-94544-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)