Flood susceptibility mapping using Fuzzy, Statistical enhancement and Machine Learning Models: a study on the Mayurakshi River Basin of eastern India
DOI:
https://doi.org/10.12775/bgeo-2026-0009Keywords
ANN, flood susceptibility, Fuzzy-Analytical Hierarchy Process, ROC curve, SVM, Partial Least SquareAbstract
The Mayurakshi River Basin (MRB) is imperilled by flooding every year and is a major flood-prone area of eastern India. The present study highlights the crucial factors triggering flood inundation and identifies the flood-susceptible zones (FSZ) in the MRB using pertinent models and a statistical-model-based supervised enhancement. The FSZs of the MRB were determined using Fuzzy Analytical Hierarchy Process (FAHP), enhanced FAHP using Partial Least Square Regression-Variable Importance in Projection (PLS-VIP-FAHP) method and machine learning (ML) algorithms, Support Vector Machine (SVM) and Artificial Neural Network (ANN). The FAHP-, PLS-VIP-FAHP-, SVM- and ANN-based FSZ outcomes were validated using the Receiver Operating Characteristics (ROC) curve with a 70:30 training–testing ratio. The SVM model registered the highest degree of accuracy, with 0.96, while PLS-VIP-FAHP registered the lowest, with 0.89. Interestingly, the accuracy metric for FAHP surpassed the same for ANN, with FAHP registering 0.93 and the latter registering 0.92. While testing with a greater number of sample points, ANN registered an AUC of 0.90, and FAHP registered 0.89. Application of ground knowledge in selecting response parameters could help mathematical models to gain significant accuracy compared to the ML algorithm.
References
AL-ABADI AM and AL-NAJAR NA, 2020, Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness. Natural Hazards 100(2): 461-491. DOI: https://doi.org/10.1007/s11069-019-03820-z.
ALIYEV R, TEMIZKAN H and ALIYEV R, 2020, Fuzzy analytic hierarchy process-based multi-criteria decision making for universities ranking. Symmetry 12(8): 1351. DOI: https://doi.org/10.3390/sym12081351.
AL-JUAIDI AE, NASSAR AM and AL-JUAIDI OE, 2018, Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arabian Journal of Geosciences 11: 1-10. DOI: https://doi.org/10.1007/s12517-018-4095-0.
ATKINSON PM and TATNALL AR, 1997, Introduction neural networks in remote sensing. International Journal of Remote Sensing 18(4): 699-709. DOI: https://doi.org/10.1080/014311697218700.
BAMMOU Y, BENZOUGAGH B, IGMOULLAN B, OUALALI A, KADER S, SPALEVIC V, PAUL S, PAOLO B and MARKOVIĆ SB, 2024, Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas. Natural Hazards 120(8): 7787-7816. DOI: https://doi.org/10.1007/s11069-024-06547-8.
Bandyopadhyay S, KAR NS, DAS S and SEN J, 2014, River systems and water resources of West Bengal: a review. Geological Society of India Special Publication 3: 63-84.
BARAL U, SAHA UD, MUKHOPADHYAY U and SINGH D, 2023, Drought risk assessment on the eastern part of Indian peninsula—a study on Purulia district, West Bengal. Environmental Monitoring and Assessment 195(11): 1364. DOI: https://doi.org/10.1007/s10661-023-11986-0.
BRITO DE MM and EVERS M, 2016, Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Natural Hazards and Earth System Sciences 16(4): 1019-1033. DOI: https://doi.org/10.5194/nhess-16-1019-2016.
CAO C, XU P, WANG Y, CHEN J, ZHENG L and NIU C, 2016, Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9): 948. DOI: https://doi.org/10.3390/su8090948.
CHATTERJEE S and JANA NC, 2022, Hydro-Meteorological Analysis of 2015 Rarh Bengal Flood in the Lower Gangetic Plain of India: Exceptional, Fast and Furious. In: Climate, Environment and Disaster in Developing Countries: 401-424. Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-16-6966-8_20.
CHEN J, HUANG G and CHEN W, 2021, Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models. Journal of Environmental Management 293: 112810. DOI: https://doi.org/10.1016/j.jenvman.2021.112810.
DAS S, 2018, Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arabian Journal of Geosciences 11(19): 576. DOI: https://doi.org/10.1007/s12517-018-3933-4.
DAS S and GUPTA A, 2021, Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geoscience Frontiers 12(5): 101206. DOI: https://doi.org/10.1016/j.gsf.2021.101206.
DODANGEH E, CHOUBIN B, EIGDIR AN, NABIPOUR N, PANAHI M, SHAMSHIRBAND S and MOSAVI A, 2020, Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of the Total Environment 705: 135983. DOI: https://doi.org/10.1016/j.scitotenv.2019.135983.
FARRÉS M, PLATIKANOV S, TSAKOVSKI S and TAULER R, 2015, Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. Journal of Chemometrics 29(10): 528-536. DOI: https://doi.org/10.1002/cem.2736.
GHOSH A and KAR SK, 2018, Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Natural Hazards 94: 349-368. DOI: https://doi.org/10.1007/s11069-018-3392-y.
GHOSH A and MAITI R, 2021, Soil erosion susceptibility assessment using logistic regression, decision tree and random forest: study on the Mayurakshi river basin of Eastern India. Environmental Earth Sciences 80: 1-16. DOI: https://doi.org/10.1007/s12665-021-09669-5.
GHOSH KG and PAL S, 2015, Impact of dam and barrage on flood trend of lower catchment of Mayurakshi river basin, eastern India. European Water 50: 3-23.
GOKCEOGLU C, SONMEZ H, NEFESLIOGLU HA, DUMAN TY and CAN T, 2005, The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Engineering Geology 81(1): 65-83. DOI: https://doi.org/10.1016/j.enggeo.2005.07.011.
GURNEY K, 1997, Multilayer nets and backpropagation. Dept. Human Sciences, Brunel University, Uxbridge, Middx. UK.
ISLAM ARMT, JIBON MJN, SIDDIK MA, MALLICK J, ALQADHI S, PAL SC, PUROHIT S and SENAPATHI V, 2025, Predicting flood risks using advanced machine learning algorithms with a focus on Bangladesh: influencing factors, gaps and future challenges. Earth Science Informatics 18(3): 300. DOI: https://doi.org/10.1007/s12145-025-01719-x.
ISLAM A and BARMAN SD, 2020, Drainage basin morphometry and evaluating its role on flood-inducing capacity of tributary basins of Mayurakshi River, India. SN Applied Sciences 2(6): 1087. DOI: https://doi.org/10.1007/s42452-020-2900-3.
ISLAM A and GHOSH S, 2021, Economic transformation in the wake of flood: a case of the lower stretch of the Mayurakshi River Basin, India. Environment, Development and Sustainability 23(10): 15550-15590. DOI: https://doi.org/10.1007/s10668-021-01311-5.
ISLAM A and SARKAR B, 2020, Analysing flood history and simulating the nature of future floods using Gumbel method and log Pearson Type III: The case of the Mayurakshi River Basin, India. Bulletin of Geography. Physical Geography Series 19(1): 43–69. DOI: https://doi.org/10.2478/bgeo-2020-0010.
ISLAM A, PAL SC, CHOWDHURI I, SALAM R, ISLAM MS, RAHMAN MM, ZAHID A and IDRIS AM, 2021a, Application of novel framework approach for prediction of nitrate concentration susceptibility in coastal multi-aquifers, Bangladesh. Science of The Total Environment 801: 149811. DOI: https://doi.org/10.1016/j.scitotenv.2021.149811.
ISLAM A, TALUKDAR S, MAHATO S, KUNDU S, EIBEK KU, PHAM QB, KURIQI A and LINH N, 2021b, Flood susceptibility modelling using advanced ensemble machine learning models. Geoscience Frontiers 12(3): 101075. DOI: https://doi.org/10.1016/j.gsf.2020.09.006.
Jafarzadegan K, MORADKHANI H, PAPPENBERGER F, MOFTAKHARI H, BATES P, ABBASZADEH P, MARSOOLI R, FERREIRA C, CLOKE HL, OGDEN F and DUAN Q, 2023, Recent advances and new frontiers in riverine and coastal flood modeling. Reviews of Geophysics 61(2): e2022RG000788. DOI: https://doi.org/10.1029/2022RG000788.
KARIMINEJAD N, HOSSEINALIZADEH M, POURGHASEMI HR, BERNATEK-JAKIEL A and ALINEJAD M, 2019, GIS-based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi-arid environment: Golestan Province, NE Iran. Land Degradation & Development 30(18): 2211-2225. DOI: https://doi.org/10.1002/ldr.3396.
KAUSHIK K, PANDEY AC, PARIDA BR and DWIVEDI CS, 2025, Geoinformatics-based watershed-level flood hazard analysis using fuzzy analytic hierarchy process across the Ganga–Brahmaputra basin. Journal of Earth System Science 134(3): 1-22. DOI: https://doi.org/10.1007/s12040-025-02593-3.
KHODAEI H, NASIRI SALEH F, NOBAKHT DALIR A and ZAREI E, 2025, Future flood susceptibility mapping under climate and land use change. Scientific Reports 15(1): 12394. DOI: https://doi.org/10.1038/s41598-025-96713-8.
KHOSRAVI K, SHAHABI H, PHAM BT, ADAMOWSKI J, SHIRZADI A, PRADHAN B, DOU J, LY HB, GRÓF G, HO HL and HONG H, 2019, A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology 573: 311-323. DOI: https://doi.org/10.1016/j.jhydrol.2019.03.073.
LIN J, SRENG C, OARE E and BATARSEH FA, 2023, NeuralFlood: an AI-driven flood susceptibility index. Frontiers in Water 5: 1291305. DOI: https://doi.org/10.3389/frwa.2023.1291305.
LYU HM, SHEN SL, YANG J and ZHOU AN, 2020, Risk Assessment of earthquake-triggered geo-hazards surrounding Wenchuan, China. Natural Hazards Review 21: 05020007. DOI: https://doi.org/10.1061/(ASCE)NH.1527-6996.0000387.
MAHAD NF, YOSUF NM and ISMAIL NF, 2019, The application of fuzzy analytic hierarchy process (FAHP) approach to solve multi-criteria decision making (MCDM) problems. Journal of Physics: Conference Series. DOI: https://doi.org/10.1088/1742-6596/1366/1/012058.
MAHMOUD SH and GAN TY, 2018, Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production 196: 216-229. DOI: https://doi.org/10.1016/j.jclepro.2018.06.047.
MISHRA V, NANDITHA JS, DANGAR S, CHUPHAL DS and VEGAD U, 2024, Drivers, changes, and impacts of hydrological extremes in India: A review. Wiley Interdisciplinary Reviews: Water 11(5): e1742. DOI: https://doi.org/10.1002/wat2.1742.
MISRA A, WHITE K, NSUTEZO SF, STRAKA III W and LAVISTA J, 2025, Mapping global floods with 10 years of satellite radar data. Nature Communications 16(1): 5762. DOI: https://doi.org/10.1038/s41467-025-60853-4.
MOORE ID, GRAYSON RB and LADSON AR, 1991, Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes 5(1): 3-30. DOI: https://doi.org/10.1002/hyp.3360050103.
MUDASHIRU RB, SABTU N, ABUSTAN I and BALOGUN W, 2021, Flood hazard mapping methods: A review. Journal of Hydrology 603: 126846. DOI: https://doi.org/10.1016/j.jhydrol.2021.126846.
MUKHERJEE A, FRYAR AE and THOMAS WA, 2009, Geologic, geomorphic and hydrologic framework and evolution of the Bengal basin, India and Bangladesh. Journal of Asian Earth Sciences 34(3): 227-244. DOI: https://doi.org/10.1016/j.jseaes.2008.05.011.
MUKHERJEE R, SENGUPTA D and SIKDAR SK, 2015, Selection of sustainable processes using sustainability footprint method: A case study of methanol production from carbon dioxide. In: Computer Aided Chemical Engineering 36: 311-329. Elsevier. DOI: https://doi.org/10.1016/B978-0-444-63472-6.00012-4.
MUKHOPADHYAY S and LET S, 2014, Changing flood intensity zone of Dwarka river basin in eastern India. Transactions 36(1).
NANDI A, MANDAL A, WILSON M and SMITH D, 2016, Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environmental Earth Sciences 75(6): 465. DOI: https://doi.org/10.1007/s12665-016-5323-0.
OFFICE OF THE DISTRICT MAGISTRATE, 2014, Flood preparedness and Management Plan, Murshidabad District Govt. of West Bengal.
OFFICE OF THE DISTRICT MAGISTRATE, 2016, District Disaster Management Plan, Murshidabad District. Govt. of West Bengal.
O’MALLEY LSS, 1914, Bengal District Gazetteers Murshidabad. Calcutta, Bengal Sekretariat Book Depot.
PAHARI S, DAS CHATTERJEE N and BARMAN NK, 2024, Noise vulnerability assessment using an integrated approach of multi-criteria decision-making model and geospatial techniques. International Journal of Environmental Science and Technology 21(6): 5421-5436. DOI: https://doi.org/10.1007/s13762-023-05394-7.
PAL S, 2016, Impact of Massanjore dam on hydro-geomorphological modification of Mayurakshi river, Eastern India. Environment, Development and Sustainability 18: 921-944. DOI: https://doi.org/10.1007/s10668-015-9676-4.
PENG G, HAN L, LIU Z, GUO Y, YAN J and JIA X, 2021, An application of fuzzy analytic hierarchy process in risk evaluation model. Frontiers in Psychology 12: 715003. DOI: https://doi.org/10.3389/fpsyg.2021.715003.
POURGHASEMI HR, PRADHAN B and GOKCEOGLU C, 2012, Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards 63: 965-996. DOI: https://doi.org/10.1007/s11069-012-0217-2.
PRADHAN B, 2010, Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology 9(2).
PRISCILLIA S, SCHILLACI C and LIPANI A, 2021, Flood susceptibility assessment using artificial neural networks in Indonesia. Artificial Intelligence in Geosciences 2: 215-222. DOI: https://doi.org/10.1016/j.aiig.2022.02.001.
RAHMATI O, POURGHASEMI HR and ZEINIVAND H, 2016, Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International 31(1): 42-70. DOI: https://doi.org/10.1080/10106049.2015.1041559.
SAHA UD, MITRA S, MONDAL M and BISWAS N, 2022, Assessment of the impact of anthropogenic stress on wetlands of the upper Ganga Delta, India: a multi criteria decision making approach. Geocarto International 37(26): 12580-12601. DOI: https://doi.org/10.1080/10106049.2022.2068670.
SALMERÓN GÓMEZ R, GARCÍA PÉREZ J, LÓPEZ MARTÍN MDM and GARCÍA CG, 2016, Collinearity diagnostic applied in ridge estimation through the variance inflation factor. Journal of Applied Statistics 43(10): 1831-1849. DOI: https://doi.org/10.1080/02664763.2015.1120712.
SALVATI A, NIA AM, SALAJEGHEH A, GHADERI K, ASL DT, AL-ANSARI N, SOLAIMANI F and CLAGUE JJ, 2023, Flood susceptibility mapping using support vector regression and hyper-parameter optimization. Journal of Flood Risk Management 16(4): e12920. DOI: https://doi.org/10.1111/jfr3.12920.
SEN A, SAHA UD, MAJUMDER A and BISWAS N, 2020, Facets of Anthropogenic Encroachment within the Palaeo-Fluvial Regime of Kana–Ghia–Kunti System, Damodar Fan Region. In: Anthropogeomorphology of Bhagirathi-Hooghly River System in India: 491-520. CRC Press.
SEN D, 2010, Flood hazards in India and management strategies. In: Natural and Anthropogenic Disasters: Vulnerability, Preparedness and Mitigation: 126-146. Dordrecht: Springer Netherlands. DOI: https://doi.org/10.1007/978-90-481-2498-5_7.
SHAHABI H, SHIRZADI A, GHADERI K, OMIDVAR E, AL-ANSARI N, CLAGUE JJ, GEERTSEMA M, KHOSRAVI K, AMINI A, BAHRAMI S and AHMAD A, 2020, Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sensing 12(2): 266. DOI: https://doi.org/10.3390/rs12020266.
SHAHABI H, SHIRZADI A, RONOUD S, ASADI S, PHAM BT, MANSOURIPOUR F, GEERTSEMA M, CLAGUE JJ and BUI DT, 2021, Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geoscience Frontiers 12(3): 101100. DOI: https://doi.org/10.1016/j.gsf.2020.10.007.
SHARMA VK, SHUKLA AK, CHOWDARY VM, SARAN S and SRIVASTAV SK, 2025, Integration of recurrent neural network models and flood inundation extent libraries for flood forecasting. Remote Sensing Applications: Society and Environment 39: 101649. DOI: https://doi.org/10.1016/j.rsase.2025.101649.
SOUISSI D, ZOUHRI L, HAMMAMI S, MSADDEK MH, ZGHIBI A and DLALA M, 2020, GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto International 35(9): 991-1017. DOI: https://doi.org/10.1080/10106049.2019.1570346.
SWAIN KC, SINGHA C and NAYAK L, 2020, Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information 9(12): 720. DOI: https://doi.org/10.3390/ijgi9120720.
TANG Z, ZHANG H, YI S and XIAO Y, 2018, Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. Journal of Hydrology 558: 144-158. DOI: https://doi.org/10.1016/j.jhydrol.2018.01.033.
TEHRANY MS, PRADHAN B, MANSOR S and AHMAD N, 2015, Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125: 91-101. DOI: https://doi.org/10.1016/j.catena.2014.10.017.
VAPNIK VN, 2013, Inductive principles of statistics and learning theory. In: Mathematical Perspectives on Neural Networks: 777-841. Psychology Press.
VOJTEK M and VOJTEKOVÁ J, 2019, Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11(2): 364. DOI: https://doi.org/10.3390/w11020364.
WOLD S, JOHANSSON E and COCCHI M, 1993, PLS: partial least squares projections to latent structures. In: 3D QSAR in Drug Design: Theory, Methods and Applications: 523-550. Kluwer ESCOM Science Publisher.
WU T, LI HC, WEI SP, CHEN WB, CHEN YM, SU YF, LIU JJ and SHIH HJ, 2016, A comprehensive disaster impact assessment of extreme rainfall events under climate change: A case study in Zheng-wen river basin, Taiwan. Environmental Earth Sciences 75(7): 597. DOI: https://doi.org/10.1007/s12665-016-5392-0.
YANG D, ZHANG T, ARABAMERI A, SANTOSH M, SAHA UD and ISLAM A, 2023, Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches. Earth Science Informatics 16(4): 3143-3161. DOI: https://doi.org/10.1007/s12145-023-01088-3.
YANG L and CERVONE G, 2019, Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Computing 23(24): 13393-13408. DOI: https://doi.org/10.1007/s00500-019-03879-7.
YANG XL, DING JH and HOU H, 2013, Application of a triangular fuzzy AHP approach for flood risk evaluation and response measures analysis. Natural Hazards 68: 657-674. DOI: https://doi.org/10.1007/s11069-013-0642-x.
YESILNACAR E and TOPAL T, 2005, Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology 79(3-4): 251-266. DOI: https://doi.org/10.1016/j.enggeo.2005.02.002.
YILMAZ NG, YURDAKUL M and GOKTAN RM, 2007, Prediction of radial bit cutting force in high-strength rocks using multiple linear regression analysis. International Journal of Rock Mechanics and Mining Sciences 44(6): 962-970. DOI: https://doi.org/10.1016/j.ijrmms.2007.02.003.
YOUSSEF AM, PRADHAN B and HASSAN AM, 2011, Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environmental Earth Sciences 62(3): 611-623. DOI: https://doi.org/10.1007/s12665-010-0551-1.
ZHU K, WANG Z, LAI C, LI S, ZENG Z and CHEN X, 2024, Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods. International Journal of Disaster Risk Science 15: 1-16. DOI: https://doi.org/10.1007/s13753-024-00590-6.
ESRI, 2024, Data classification methods. Available at: https://pro.arcgis.com/en/proapp/latest/help/mapping/layer-properties/data-classification-methods.htm.
WANG G, LIU L, SHI P, ZHANG G and LIU J, 2021, Flood Risk Assessment of Metro System Using Improved Trapezoidal Fuzzy AHP: A Case Study of Guangzhou. Remote Sensing 13: 5154. DOI: https://doi.org/10.3390/rs13245154.
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