Abstract
Floods are the most recurrent weather-related natural disaster causing widespread devastation. In India, about 12% of the land is prone to flood and river erosion where Bihar is the most flood-prone state. The impact of floods becomes more pronounced in areas where population density is relatively high. The study was carried out to prepare a model to identify different levels of flood susceptible zones. A set of multi-sourced geospatial data such as slope, elevation, curvature, rainfall, drainage density, proximity to the river, soil types, land use, stratigraphy, Topographic Ruggedness Index, Sediment Transport Index, and Topographic Wetness Index were considered for the modeling. A flood susceptibility map was produced using Shannon’s entropy model in which the receiver-operating characteristics curve achieved 0.87 accuracy indicating high precision. The outcome of the study demonstrates five different zones of very high, high, medium, low, and very low susceptibility. It was found that very high and high susceptible zones constitute about 52% of the total area that needs a special attention. The findings of the study can be useful to planners and researchers for flood management strategies.
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References
Abedi R, Costache R, Shafizadeh-Moghadam H, Pham QB (2021) Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int. https://doi.org/10.1080/10106049.2021.1920636
Abrams M, Crippen R, Fujisada H (2020) ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD). Remote Sens 12:1–12. https://doi.org/10.3390/rs12071156
Agrawal SK, Kharya AK, Khattar R (2001) Flood Management in Bagmati Basin. Central Water Commission, Bihar, India
Ahadzie DK, Mensah H, Simpeh E (2021) Impact of floods, recovery, and repairs of residential structures in Ghana: insights from homeowners. GeoJournal. https://doi.org/10.1007/s10708-021-10425-2
Åhäll KI, Connelly JN, Brewer TS (2000) Episodic rapakivi magmatism due to distal orogenesis? Correlation of 1.69–1.50 Ga orogenic and inboard, “anorogenic” events in the Baltic Shield. Geology 28:823–826. https://doi.org/10.1130/0091-7613(2000)28%3c823:ERMDTD%3e2.0.CO;2
Ahmadlou M, Al-Fugara A, Al-Shabeeb AR, Arora A, Al-Adamat R, Pham QB, Al-Ansari N, Linh NT, Sajedi H (2021) Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. J Flood Risk Manag 14:1–22. https://doi.org/10.1111/jfr3.12683
Alam A, Ahmed B, Sammonds P (2020) Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Q Int. https://doi.org/10.1016/j.quaint.2020.04.047
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 Geo-Information 10:252. https://doi.org/10.3390/ijgi10040252
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
Ali SA, Parvin F, Pham QB, Vojtek M, Vojtekova J, Costache R, Linh NT, Nguyen HQ, Ahmed 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 Indic 117:106620. https://doi.org/10.1016/j.ecolind.2020.106620
Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ (2012) SWAT: model use, calibration, and validation. Biol Syst Eng 55:1491–1508. https://doi.org/10.13031/2013.42256
Arumugum J, Anbazhagan S (2016) Flood susceptibility appraisal in Ponnaiyar River Basin, India using frequency ratio (FR) and Shannon’s Entropy (SE) models. Int J Adv Rem Sens GIS 5(10):1946–1962
Azareh A, Sardooi ER, Choubin B, Barkhori S, Shahdadi A, Adamowski J, Shamshirband S (2019) Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment. Geocarto Int. https://doi.org/10.1080/10106049.2019.1695958
Basukala AK, Oldenburg C, Schellberg J, Sultanov M, Dubovyk O (2017) Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches. Eur J Remote Sens 50:187–201. https://doi.org/10.1080/22797254.2017.1308235
Bhatt CM, Rao GS, Jangam S (2020) Detection of urban flood inundation using RISAT-1 SAR images: a case study of Srinagar, Jammu and Kashmir (North India) floods of September 2014. Model Earth Syst Environ 6:429–438. https://doi.org/10.1007/s40808-019-00690-z
Bhatt CM, Gupta A, Roy A, Dalal P, Chauhan P (2021) Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data. Geomat Nat Hazards Risk 12:84–102. https://doi.org/10.1080/19475705.2020.1861113
Bihar-Government (2020) Bihar State Disaster Management Authority. http://bsdma.org/Know-Your-Risk.aspx?id=3. Accessed 10 Nov 2020
Bui DT, Khosravi K, Shahabi H, Daggupati B, Adamowski JF, Melesse AM, Pham BT, Pourghasemi HR, Mahmoudi M, Bahrami S, Pradhan B, Shirzadi S, 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. https://doi.org/10.3390/rs11131589
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 8:948. https://doi.org/10.3390/su8090948
Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, Xiao L, Xie X, Ahmad BB (2018) GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ 634:853–867. https://doi.org/10.1016/j.scitotenv.2018.04.055
Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
Costache R (2019) Flood susceptibility assessment by using bivariate statistics and machine learning models—a useful tool for flood risk management. Water Resour Manag 33:3239–3256. https://doi.org/10.1007/s11269-019-02301-z
Costache R, Tien D (2020) Science of the total environment identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Sci Total Environ 712:136492. https://doi.org/10.1016/j.scitotenv.2019.136492
Costache R, Zaharia L (2017) Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment—case study: Bâsca chiojdului river catchment (Romania). J Earth Syst Sci 126:1–19. https://doi.org/10.1007/s12040-017-0828-9
Dandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ 3:1627–1646. https://doi.org/10.1007/s40808-017-0388-7
Das S (2020) Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2020.100379
Dejen A, Soni S (2020) Flash flood risk assessment using geospatial technology in Shewa Robit town, Ethiopia. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-01016-0
Falah F, Rahmati O, Rostami M, Ahmadisharaf E, Daliakopoulos IN, Pourghasemi HR (2019) Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. Elsevier Inc., Amsterdam
Fan S, Swift DJP, Traykovski P, Bentley S, Borgeld JC, Reed CW, Niedoroda AW (2004) River flooding, storm resuspension, and event stratigraphy on the northern California shelf: Observations compared with simulations. Mar Geol 210:17–41. https://doi.org/10.1016/j.margeo.2004.05.024
FMIS (2020a) FMIS. http://fmis.bih.nic.in/Riverbasin.html. Accessed 1 Nov 2020
FMIS (2020b) FMIS. http://fmis.bih.nic.in/history.html. Accessed 1 Nov 2020
Fortin JP, Turcotte R, Massicotte S, Turcotte R, Massicotte S, Moussa R, Fitzback J, Villeneuve JP (2001) Distributed watershed model compatible with remote sensing and GIS data. I: description of model. J Hydrol Eng 6:91–99
Gudiyangada Nachappa T, Meena SR (2020) A novel per pixel and object-based ensemble approach for flood susceptibility mapping. Geom Nat Hazards Risk 11:2147–2175. https://doi.org/10.1080/19475705.2020.1833990
Guo J, Wu X, Wei G (2020) A new economic loss assessment system for urban severe rainfall and flooding disasters based on big data fusion. Environ Res 188:109822. https://doi.org/10.1016/j.envres.2020.109822
Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126:39. https://doi.org/10.1007/s12040-017-0819-x
Hallegatte S, Bangalore M, Bonzanigo L, Fay M, Kane T, Narloch U, Rozenberg J, Treguer D, Vogt-Schilb A (2016) Shock waves: managing the impacts of climate change on poverty. The World Bank
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36. https://doi.org/10.2196/jmir.9160
Horton RE (1932) Drainage-basin characteristics. EOS Trans Am Geophys Union 13:350–361. https://doi.org/10.1029/TR013i001p00350
Hosseini FS, Choubin B, Mosavi A, Nabipour N, Shamshirband S, Darabi H, Haghighi AT (2020) Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method. Sci Total Environ 711:135161. https://doi.org/10.1016/j.scitotenv.2019.135161
Jalayer F, De Risi R, De Paola F, Giugni M, Manfredi G, Gasparini P, Topa ME, Yonas N, Yeshitela K, Nebebe A, Cavan G, Lindley S, Printz A, Renner F (2014) Probabilistic GIS-based method for delineation of urban flooding risk hotspots. Nat Hazards 73:975–1001. https://doi.org/10.1007/s11069-014-1119-2
Karmakar S, Simonovic SP, Peck A, Black J (2010) An information system for risk-vulnerability assessment to flood. J Geogr Inf Syst 02:129–146. https://doi.org/10.4236/jgis.2010.23020
Karmokar S, De M (2020) Flash flood risk assessment for drainage basins in the Himalayan foreland of Jalpaiguri and Darjeeling Districts, West Bengal. Model Earth Syst Environ 6:2263–2289. https://doi.org/10.1007/s40808-020-00807-9
Khan MK, Ahmad S (2017) Flood resistant buildings: a requirement for sustainable development in flood prone areas. Int J Emerg Technol 8:114–116
Khoirunisa N, Ku CY, Liu CY (2021) A GIS-based artificial neural network model for flood susceptibility assessment. Int J Environ Res Public Health 18:1–20. https://doi.org/10.3390/ijerph18031072
Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016a) 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, Pourghasemi HR, Chapi K, Bahri M (2016b) Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ Monit Assess 188:656. https://doi.org/10.1007/s10661-016-5665-9
Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Bui DT (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266
Khosravi K, Shahabi H, Pham BT, Adamawoski J, Shirzadi A, Pradhan B, Dou J, Ly H, 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
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 67:251–264. https://doi.org/10.1007/s12665-011-1504-z
Komolafe AA, Olorunfemi IE, Akinluyi FO, Adeyemi MA, Ajay JA (2021) Enhanced flood hazard modelling using hydraulic, analytical hierarchical process and height above nearest drainage models in Ogunpa river basin, Ibadan, Southwestern Nigeria. Model Earth Syst Environ 7:967–981. https://doi.org/10.1007/s40808-020-01037-9
Konadu DD, Fosu C (2009) Digital elevation models and GIS for watershed modelling and flood prediction—a case study of Accra Ghana. Springer, Dordrecht, pp 325–332. https://doi.org/10.1007/978-1-4020-9139-1_31
Kumar K, Singh V, Roshni T (2020) Efficacy of the hybrid neural networks in statistical downscaling of precipitation of the Bagmati River basin. J Water Clim Change 11:1302–1322. https://doi.org/10.2166/wcc.2019.259
Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990. https://doi.org/10.1007/s00254-005-1228-z
Lee S, Kim JC, Jung HS, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geom Nat Hazards Risk 8:1185–1203. https://doi.org/10.1080/19475705.2017.1308971
Lee M, Kang J, Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: International geoscience and remote sensing symposium (IGARSS), pp 895–898
Lin J (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inf Theory 37:145–151. https://doi.org/10.1109/18.61115
Mahmoody Vanolya N, Jelokhani-Niaraki M (2021) The use of subjective–objective weights in GIS-based multi-criteria decision analysis for flood hazard assessment: a case study in Mazandaran, Iran. GeoJournal 86:379–398. https://doi.org/10.1007/s10708-019-10075-5
Manfreda S, Di Leo M, Sole A (2011) Detection of flood-prone areas using digital elevation models. J Hydrol Eng 16:781–790. https://doi.org/10.1061/(asce)he.1943-5584.0000367
Merz B, Thieken AH, Gocht M (2007) Flood risk mapping at the local scale: concepts and challenges. Springer, Berlin, pp 231–251
Mirdda HA, Bera S, Siddiqui MA, Singh B (2020) Analysis of bi-variate statistical and multi-criteria decision-making models in landslide susceptibility mapping in lower Mandakini Valley, India. GeoJournal 85:681–701. https://doi.org/10.1007/s10708-019-09991-3
Mirzaei S, Vafakhah M, Pradhan B, Alavi SJ (2020) Flood susceptibility assessment using extreme gradient boosting (EGB), Iran. Earth Sci Inform. https://doi.org/10.1007/s12145-020-00530-0
Mishra DK (2011) Bagmati: the sorrow of North Bihar. Frontierweekly 44(2):1–4
Mojaddadi H, Pradhan B, Nampak H, Ahmed N, Ghazali AH (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geom Nat Hazards Risk 8:1080–1102. https://doi.org/10.1080/19475705.2017.1294113
Moore ID, Burch GJ (1986) Physical basis of the length-slope factor in the universal soil loss equation. Soil Sci Soc Am J 50:1294–1298. https://doi.org/10.2136/sssaj1986.03615995005000050042x
Morita M (2014) Flood risk impact factor for comparatively evaluating the main causes that contribute to flood risk in urban drainage areas. Water (switzerland) 6:253–270. https://doi.org/10.3390/w6020253
Mukherjee F, Singh D (2020) Detecting flood prone areas in Harris County: a GIS based analysis. GeoJournal 85:647–663. https://doi.org/10.1007/s10708-019-09984-2
Nachappa TG, Piralilou ST, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer theory. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125275
Naimi B, Araújo MB (2016) SDM: a reproducible and extensible R platform for species distribution modelling. Ecography (COP) 39:368–375. https://doi.org/10.1111/ecog.01881
Namara WG, Damisse TA, Tufa FG (2021) Application of HEC-RAS and HEC-GeoRAS model for flood inundation mapping, the case of Awash Bello Flood Plain, Upper Awash River Basin, Oromiya Regional State, Ethiopia. Model Earth Syst Environ. https://doi.org/10.1007/s40808-021-01166-9
Nhu V, Mohammadi A, Shahabi H, Bin AB (2020) Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. Int J Environ Res Public Health 17:1–23. https://doi.org/10.3390/ijerph17144933
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343. https://doi.org/10.1016/S0013-7952(03)00069-3
Opolot E (2013) Application of remote sensing and geographical information systems in flood management: a review. Res J Appl Sci Eng Technol 6:1884–1894. https://doi.org/10.19026/rjaset.6.3920
Patton PC, Baker VR (1976) Morphometry and floods in small drainage basins subject to diverse hydrogeomorphic controls. Water Resour Res 12:941–952. https://doi.org/10.1029/WR012i005p00941
Pham BT, Jaafari A, Van PT, Yen HPH, Tuyen TT, Loung VV, Nguyen HD, Le HV, Foong LK (2021) Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci Front 12:101105. https://doi.org/10.1016/j.gsf.2020.11.003
Pirnia A, Golshan M, Darabi H, Adamowski J, Rozbeh S (2019) Using the mann–kendall test and double mass curve method to explore stream flow changes in response to climate and human activities. J Water Clim Change 10:725–742. https://doi.org/10.2166/wcc.2018.162
Prabnakorn S, Suryadi FX, Chongwilaikasem J, de Fraiture C (2019) Development of an integrated flood hazard assessment model for a complex river system: a case study of the Mun River Basin, Thailand. Model Earth Syst Environ 5:1265–1281. https://doi.org/10.1007/s40808-019-00634-7
Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9:1–18
Rahmati O, Pourghasemi HR, Zeinivand H (2016a) 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
Rahmati O, Zeinivand H, Besharat M (2016b) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geom Nat Hazards Risk 7:1000–1017. https://doi.org/10.1080/19475705.2015.1045043
Rastogi AK, Thakur PK, Rao GS, Aggarwal SP, Dadhwal VK, Chauhan P (2018) Integrated flood study of Bagmati river basin with hydro processing, flood inundation mapping & 1-D hydrodynamic modeling using remote sensing and GIS. ISPRS Ann Photogramm Remote Sens Spat Inf Sci IV:165–172. https://doi.org/10.5194/isprs-annals-IV-5-165-2018
Regmi NR, Giardino JR, Vitek JD (2010) Geomorphology modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187. https://doi.org/10.1016/j.geomorph.2009.10.002
Rentschler J, Salhab M (2020) People in harm’s way. flood exposure and poverty in 189 countries. The World Bank
Riley SJ, DeGloria SD, Elliot R (1999) Index that quantifies topographic heterogeneity. Intermt J Sci 5:23–27
Rubinato M, Nichols A, Peng Y, Zhang J, Lashford C, Cai Y, Lin P, Tait S (2019) Urban and river flooding: Comparison of flood risk management approaches in the UK and China and an assessment of future knowledge needs. Water Sci Eng 12:274–283. https://doi.org/10.1016/j.wse.2019.12.004
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
Santos PP, Reis E, Pereira S, Santos M (2019) A flood susceptibility model at the national scale based on multicriteria analysis. Sci Total Environ 667:325–337. https://doi.org/10.1016/j.scitotenv.2019.02.328
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 12:101100. https://doi.org/10.1016/j.gsf.2020.10.007
Talukdar S, Ghose B, Shahfahad SR, Mahato S, Pham QB, Linh NTL, Costache R, Avand M (2020) Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stoch Environ Res Risk Assess 34:2277–2300. https://doi.org/10.1007/s00477-020-01862-5
Tamiru H, Wagari M (2021) Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia. Model Earth Syst Environ. https://doi.org/10.1007/s40808-021-01175-8
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, Pradhan B, Jebur MN (2014a) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Tehrany MS, Pradhan B, Mansor S, Ahmad N (2014b) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125:91–101. https://doi.org/10.1016/j.catena.2014.10.017
Tehrany MS, Shabani F, Jebur MN, Hong H, Chen W, Xie X (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geom Nat Hazards Risk 8:1538–1561. https://doi.org/10.1080/19475705.2017.1362038
Tehrany MS, Jones S, Shabani F (2019) Catena Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. CATENA 175:174–192. https://doi.org/10.1016/j.catena.2018.12.011
Thilagavathi G, Tamilenthi S, Ramu C, Baskaran R (2011) Application of GIS in flood hazard zonation studies in Papanasam Taluk, Thanjavur District, Tamilnadu. Adv Appl Sci Res 2:574–585
Tiwary NK (2016) Real time flood forecasting and flood inundation mapping for Bagmati river system of Bihar, India. Indian Institute of Technology Delhi
Tunusluoglu MC, Gokceoglu C, Nefeslioglu HA, Sonmez H (2008) Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environ Geol 54:9–22. https://doi.org/10.1007/s00254-007-0788-5
UNSD (2015) UNSD. https://www.un.org/sustainabledevelopment/blog/tag/floods/. Accessed 14 Oct 2020
Vignesh KS, Anandakumar I, Ranjan R, Borah D (2021) Flood vulnerability assessment using an integrated approach of multi-criteria decision-making model and geospatial techniques. Model Earth Syst Environ 7:767–781. https://doi.org/10.1007/s40808-020-00997-2
Wallemacq P, Debarati G-S, McClean D (2015) The human costs of weather related disasters: 1995–2015. Centre for Research on the Epidemiology of Disasters (CRED); United Nations Office for Disaster Risk Reduction (UNISDR). https://doi.org/10.13140/RG.2.2.17677.33769. Accessed April 2021
Werner MGF, Hunter NM, Bates PD (2005) Identifiability of distributed floodplain roughness values in flood extent estimation. J Hydrol 314:139–157. https://doi.org/10.1016/j.jhydrol.2005.03.012
WRD-Bihar (2020) WRD, Bihar, India. http://wrd.bih.nic.in/. Accessed 1 Nov 2020
Yamani K, Hazzab A, Sekkoum M, Slimane T (2016) Mapping of vulnerability of flooded area in arid region. Case study: area of Ghardaïa-Algeria. Model Earth Syst Environ 2:147. https://doi.org/10.1007/s40808-016-0183-x
Yariyan P, Avand M, Abbaspour RA et al (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geom Nat Hazards Risk 11:2282–2314. https://doi.org/10.1080/19475705.2020.1836036
Yulianto F, Fitriana HL, Sukowati KAD (2020) Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia. Model Earth Syst Environ 6:383–395. https://doi.org/10.1007/s40808-019-00686-9
Acknowledgements
The first author is thankful to the University Grant Commission (UGC) for providing Junior Research Fellowship (JRF) for the doctoral research. The authors are also thankful to the Geological Survey of India, Bihar Disaster Management agencies, NASA, Climatic Research Unit (CRU), and the United States Geological Survey (USGS) for providing necessary data freely.
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Islam, S., Tahir, M. & Parveen, S. GIS-based flood susceptibility mapping of the lower Bagmati basin in Bihar, using Shannon’s entropy model. Model. Earth Syst. Environ. 8, 3005–3019 (2022). https://doi.org/10.1007/s40808-021-01283-5
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DOI: https://doi.org/10.1007/s40808-021-01283-5