Abstract
Due to its geographical location, Northeast India (NEI) is prone to water-related disasters. The high precipitation in Assam, an NEI state, results in annual severe floods and delays in the region’s overall development. The purpose of this work is to create a flood susceptibility map for the flood-prone district of Dibrugarh in Assam using the analytical hierarchy process (AHP), a multi-criteria decision-making approach. The eight indicators, i.e., elevation, slope, drainage density (DD), distance to river (DR), topographic wetness index (TWI), rainfall intensity (RI), normalized difference vegetation index (NDVI) and stream power index (SPI) were considered as the essential flood conditioning parameters. The multicollinearity statistics were employed to erase the issues regarding highly correlated parameters. The model’s efficiency was judged by applying the ROC-AUC to analyze the better-suited model for mapping the flood susceptibility. The sensitivity results showed that the most effective flood causing factor is the elevation (46.27%) and about 36% of the study area belongs to high and very-high flood-prone areas. The validation of the flood susceptibility model is found good enough with an AUC value of 74%. The findings and conclusions of this study may assist policymakers in estimating flood hazards and mitigating them in the study area.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Linh NTT, Nguyen HQ, Ahmad A, Ghorbani MA (2020) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, Naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Ecol Ind 117(June):106620. https://doi.org/10.1016/j.ecolind.2020.106620
Andualem TG, Demeke GG (2019) Groundwater potential assessment using GIS and remote sensing: a case study of Guna tana landscape, upper blue Nile Basin, Ethiopia. J Hydrol Reg Stud 24. https://doi.org/10.1016/j.ejrh.2019.100610
Basak A, Das J, Rahman ATM, Pham QB (2021) An integrated approach for delineating and characterizing groundwater depletion hotspots in a coastal state of India. J Geol Soc India 97(11):1429–1440. https://doi.org/10.1007/s12594-021-1883-z
Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability (Switzerland) 8(9). https://doi.org/10.3390/su8090948
Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95. https://doi.org/10.1016/j.envsoft.2017.06.012
Choudhury S, Basak A, Biswas S, Das J (2022) Flash flood susceptibility mapping using GIS-based AHP method. In: Pradhan B, Shit PK, Bhunia GS, Adhikary PP, Pourghasemi HR (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_8
Das J, Gayen A, Saha S, Bhattacharya SK (2017) Modelling of alternative crops suitability to tobacco based on analytical hierarchy process in Dinhata subdivision of Koch Bihar district, West Bengal. Model Earth Syst Environ 3(4):1571–1587. https://doi.org/10.1007/s40808-017-0392-y
Eguaroje O, Alaga T, Ogbole J, Omolere S, Alwadood J, Kolawole I, Muibi KH, Nnaemeka D, Popoola DS, Samson SA, Adewoyin JE (2015) Flood vulnerability assessment of Ibadan city, Oyo state, Nigeria. World Environ 5(4)
Fenta AA, Kifle A, Gebreyohannes T, Hailu G (2015) Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia. Hydrogeol J 23(1):195–206. https://doi.org/10.1007/s10040-014-1198-x
Fernández DS, Lutz MA (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111(1–4). https://doi.org/10.1016/j.enggeo.2009.12.006
Jain SK, Kumar V, Saharia M (2013) Analysis of rainfall and temperature trends in northeast India. Int J Climatol 33(4). https://doi.org/10.1002/joc.3483
Mitra R, Das J (2022) A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS VIKOR and EDAS techniques in the Sub-Himalayan foothills region of Eastern India. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-23168-5
Mitra R, Saha P, Das J (2022) Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India. GeomatS, Nat Hazards Risk 13(1):2183–2226
Mousavi SZ, Kavian A, Soleimani K, Mousavi SR, Shirzadi A (2011) GIS-based spatial prediction of landslide susceptibility using logistic regression model. Geomatics Nat Hazards Risk 2(1). https://doi.org/10.1080/19475705.2010.532975
Msabi MM, Makonyo M (2021) Flood susceptibility mapping using GIS and multi-criteria decision analysis: a case of Dodoma region, central Tanzania. Remote Sens Appl Soc Environ 21:100445. https://doi.org/10.1016/j.rsase.2020.100445
Mu E, Pereyra-Rojas M (2018) Practical decision making using super decisions. Springer
Mukherjee I, Singh UK (2020) Delineation of groundwater potential zones in a drought-prone semi-arid region of east India using GIS and analytical hierarchical process techniques. CATENA 194(May):104681. https://doi.org/10.1016/j.catena.2020.104681
Pham BT, Shirzadi A, Tien Bui D, Prakash I, Dholakia MB (2018) A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: a case study in the Himalayan area, India. Int J Sedim Res 33(2). https://doi.org/10.1016/j.ijsrc.2017.09.008
Praveen B, Talukdar S, Shahfahad, Mahato S, Mondal J, Sharma P, Islam ARMT, Rahman A (2020) Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-67228-7
Rahmati O, Zeinivand H, Besharat M (2016) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics Nat Hazards Risk 7(3). https://doi.org/10.1080/19475705.2015.1045043
Saaty RW (1987). The analytic hierarchy process-what it is and how it is used. Math Model 9(3–5). https://doi.org/10.1016/0270-0255(87)90473-8
Saaty TL (1990) How to make a decision: the analytic hierarchy process. European J Oper Res 48(1). https://doi.org/10.1016/0377-2217(90)90057-I
Saha S, Das J, Mandal T (2022) Investigation of the watershed hydro-morphologic characteristics through the morphometric analysis: a study on Rayeng basin in Darjeeling Himalaya. Environ Challenges 100463. https://doi.org/10.1016/j.envc.2022.100463
Shahabi H, Shirzadi A, Ronoud S, Asadi S, Pham BT, Mansouripour F, Geertsema M, Clague JJ, Bui DT (2021) Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geosci Front 12(3):101100. https://doi.org/10.1016/j.gsf.2020.10.007
Shirzadi A, Chapi K, Shahabi H, Solaimani K, Kavian A, Ahmad BB (2017). Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio. Environ Earth Sci 76(4). https://doi.org/10.1007/s12665-017-6471-6
Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2020) GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int 35(9):991–1017. https://doi.org/10.1080/10106049.2019.1566405
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. https://doi.org/10.1016/j.jhydrol.2013.09.034
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). https://doi.org/10.1007/s12665-015-4830-8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bora, S.L., Das, J., Bhuyan, K., Hazarika, P.J. (2023). Flood Susceptibility Mapping Using GIS and Multi-criteria Decision Analysis in Dibrugarh District of Assam, North-East India. In: Das, J., Bhattacharya, S.K. (eds) Monitoring and Managing Multi-hazards. GIScience and Geo-environmental Modelling. Springer, Cham. https://doi.org/10.1007/978-3-031-15377-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-15377-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15376-1
Online ISBN: 978-3-031-15377-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)