Evaluation of bivariate statistical and hybrid models for the preparation of flood hazard susceptibility maps in the Brahmani River Basin, India

Aditya Kumar Anand; Sarada Prasad Pradhan

doi:10.1007/s12665-023-11069-w

journal article Aug 01, 2023

Evaluation of bivariate statistical and hybrid models for the preparation of flood hazard susceptibility maps in the Brahmani River Basin, India

Aditya Kumar Anand

Sarada Prasad Pradhan

Environmental Earth Sciences Vol. 82 No. 16 · Springer Science and Business Media LLC

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References

98

[1]

Abdelkarim A, Al-Alola SS, Alogayell HM et al (2020) Mapping of gis-flood hazard using the geomorphometric-hazard model: case study of the al-shamal train pathway in the city of qurayyat, kingdom of Saudi Arabia. Geosciences (switzerland) 10:1–32. https://doi.org/10.3390/geosciences10090333 10.3390/geosciences10090333

[2]

Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25:9325–9346. https://doi.org/10.1007/s00500-021-05903-1 10.1007/s00500-021-05903-1

[3]

Al-Abadi AM (2018) Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arab J Geosci 11:1–19. https://doi.org/10.1007/S12517-018-3584-5/TABLES/7 10.1007/s12517-018-3584-5/tables/7

[4]

Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. https://doi.org/10.1016/j.cageo.2012.03.003 10.1016/j.cageo.2012.03.003

[5]

Arabameri A, Rezaei K, Cerdà A et al (2019) A comparison of statistical methods and multicriteria 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 10.1016/j.scitotenv.2019.01.021

[6]

Arora A, Pandey M, Siddiqui MA et al (2021) Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models. Geocarto Int 36:2085–2116. https://doi.org/10.1080/10106049.2019.1687594 10.1080/10106049.2019.1687594

[7]

Asia Region S (2008) Document of the world bank climate change impacts in drought and flood affected areas: case studies in India

[8]

Axelsson C, Giove S, Soriani S (2021) Urban pluvial flood management part 1: implementing an ahp-topsis multicriteria decision analysis method for stakeholder integration in urban climate and stormwater adaptation. Water (switzerland) 13:2422. https://doi.org/10.3390/w13172422 10.3390/w13172422

[9]

Bahinipati CS (2014) Assessment of vulnerability to cyclones and floods in Odisha, India: a district-level analysis

[10]

Bhaduri A (2011) Man-made floods in Orissa in September 2011—Key issues raised by Water Initiatives Orissa. https://www.indiawaterportal.org/articles/man-made-floods-orissa-september-2011-key-issues-raised-water-initiatives-orissa. Accessed 7 Sept 2022

[11]

Bilham R, Bali BS (2014) A ninth century earthquake-induced landslide and flood in the Kashmir Valley, and earthquake damage to Kashmir’s Medieval temples. Bull Earthq Eng 12:79–109. https://doi.org/10.1007/s10518-013-9504-x 10.1007/s10518-013-9504-x

[12]

Billi P, Alemu YT, Ciampalini R (2015) Increased frequency of flash floods in Dire Dawa, Ethiopia: change in rainfall intensity or human impact? Nat Hazards J Int Soc Prevent Mitigat Natural Hazards 76:1373–1394. https://doi.org/10.1007/S11069-014-1554-0 10.1007/s11069-014-1554-0

[13]

Bui DT, 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 (basel) 11:1589. https://doi.org/10.3390/rs11131589 10.3390/rs11131589

[14]

Chen W, Hong H, Li S et al (2019) Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. J Hydrol (amst) 575:864–873. https://doi.org/10.1016/j.jhydrol.2019.05.089 10.1016/j.jhydrol.2019.05.089

[15]

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 10.1016/j.asr.2019.12.003

[16]

Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India

Sumit Das

Arabian Journal of Geosciences 2018 10.1007/s12517-018-3933-4

[17]

Das S (2019) Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl 14:60–74. https://doi.org/10.1016/j.rsase.2019.02.006 10.1016/j.rsase.2019.02.006

[18]

Das S, Gupta A (2021) Multicriteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geosci Front 12:101206. https://doi.org/10.1016/j.gsf.2021.101206 10.1016/j.gsf.2021.101206

[19]

Das J, Jha S, Goyal MK (2020) On the relationship of climatic and monsoon teleconnections with monthly precipitation over meteorologically homogenous regions in India: Wavelet & global coherence approaches. Atmos Res 238:104889. https://doi.org/10.1016/j.atmosres.2020.104889 10.1016/j.atmosres.2020.104889

[20]

Dash R (2014) Bari remains cut off from the rest of the world. Odisha Channel. https://odishachannel.com/index.php/1214/bari-remains-cut-off-from-the-rest-of-the-world/. Accessed 7 Sept 2022

[21]

Ghasemlounia R, Utlu M (2021) Flood prioritization of basins based on geomorphometric properties using principal component analysis, morphometric analysis and Redvan’s priority methods: a case study of Harşit River basin. J Hydrol (amst) 603:127061. https://doi.org/10.1016/j.jhydrol.2021.127061 10.1016/j.jhydrol.2021.127061

[22]

Ghosh A, Dey P, Ghosh T (2022a) Integration of RS-GIS with frequency ratio, fuzzy logic, logistic regression and decision tree models for flood susceptibility prediction in lower Gangetic Plain: a study on Malda District of West Bengal, India. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-022-01560-5 10.1007/s12524-022-01560-5

[23]

Ghosh A, Roy MB, Roy PK (2022b) Flood susceptibility mapping using the frequency ratio (FR) model in the Mahananda River Basin, West Bengal, India. In: Springer Climate. Springer Science and Business Media B.V., pp 73–96 10.1007/978-3-030-94395-0_3

[24]

Guhathakurta P, Sreejith OP (2011) Menon PA (2011) Impact of climate change on extreme rainfall events and flood risk in India. J Earth Syst Sci 120:359–373. https://doi.org/10.1007/S12040-011-0082-5 10.1007/s12040-011-0082-5

[25]

Guo C, Montgomery DR, Zhang Y et al (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110. https://doi.org/10.1016/j.geomorph.2015.07.012 10.1016/j.geomorph.2015.07.012

[26]

Forecasting flood-prone areas using Shannon’s entropy model

Ali Haghizadeh, Safoura Siahkamari, Amir Hamzeh Haghiabi et al.

Journal of Earth System Science 2017 10.1007/s12040-017-0819-x

[27]

Hettiarachchi S, Wasko C, Sharma A (2018) Increase in flood risk resulting from climate change in a developed urban watershed—the role of storm temporal patterns. Hydrol Earth Syst Sci 22:2041–2056. https://doi.org/10.5194/hess-22-2041-2018 10.5194/hess-22-2041-2018

[28]

Ilinca V, Gheuca I (2011) The Red Lake landslide (Ucigașu Mountain) SLIDE-MAP—an intelligent platform for landslide risk mapping in near-real time view project GEOBIOHAS View project Viorel Ilinca Geological Institute of Romania The Red Lake Landslide (Ucigaşu Mountain, Romania)

[29]

India.com News Desk (2017) Odisha: Flood Situation In Jajpur, Kendrapada Districts Continues To Be Grim. https://www.india.com/news/india/odisha-flood-situation-in-jajpur-kendrapada-districts-continues-to-be-grim-2367192/ Accessed 10 Sept 2022

[30]

Ishizaka A, Labib A (2009) Analytic hierarchy process and expert choice: benefits and limitations. Or Insight 22:201–220. https://doi.org/10.1057/ori.2009.10 10.1057/ori.2009.10

[31]

Islam A, Chandra Das B, Mahammad S, et al (2021) Deforestation and its impact on sediment flux and channel morphodynamics of the Brahmani River Basin, India. Forest Resour Resilience Conflicts 377–415. https://doi.org/10.1016/B978-0-12-822931-6.00029-0 10.1016/b978-0-12-822931-6.00029-0

[32]

Jain SK, Agarwal PK, Singh VP (2007) Hydrology and water resources of India

[33]

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 multicriteria decision-making technique. Nat Hazards 83:947–987. https://doi.org/10.1007/s11069-016-2357-2 10.1007/s11069-016-2357-2

[34]

A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

Khabat Khosravi, Himan Shahabi, Binh Thai Pham et al.

Journal of Hydrology 2019 10.1016/j.jhydrol.2019.03.073

[35]

Kia MB, Pirasteh S, Pradhan B et al (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/TABLES/4 10.1007/s12665-011-1504-z/tables/4

[36]

Konhauser KO, Powell MA, Fyfe WS et al (1997) Trace element geochemistry of river sediment, Orissa State, India. J Hydrol (amst) 193:258–269. https://doi.org/10.1016/S0022-1694(96)03146-0 10.1016/s0022-1694(96)03146-0

[37]

Assessment of climate extremes in the Eastern Mediterranean

E. Kostopoulou, P. D. Jones

Meteorology and Atmospheric Physics 2005 10.1007/s00703-005-0122-2

[38]

Lee MJ, Kang JE, Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. Int Geosci Remote Sens Symp (IGARSS) 895–898. https://doi.org/10.1109/IGARSS.2012.6351414 10.1109/igarss.2012.6351414

[39]

Li GF, Xiang XY, Tong YY, Wang HM (2013) Impact assessment of urbanization on flood risk in the Yangtze River Delta. Stoch Env Res Risk Assess 27:1683–1693. https://doi.org/10.1007/s00477-013-0706-1 10.1007/s00477-013-0706-1

[40]

Liu H, Wang Y, Zhang C et al (2018) Assessing real options in urban surface water flood risk management under climate change. Nat Hazards 94:1–18. https://doi.org/10.1007/s11069-018-3349-1 10.1007/s11069-018-3349-1

[41]

Mahato PK, Singh D, Bharati B et al (2022) Assessing the impacts of human interventions and climate change on fluvial flooding using CMIP6 data and GIS-based hydrologic and hydraulic models. Geocarto Int 37:11483–11508. https://doi.org/10.1080/10106049.2022.2060311 10.1080/10106049.2022.2060311

[42]

Mbow C, Mertz O, Diouf A et al (2008) The history of environmental change and adaptation in eastern Saloum–Senegal—driving forces and perceptions. Glob Planet Change 64:210–221. https://doi.org/10.1016/J.GLOPLACHA.2008.09.008 10.1016/j.gloplacha.2008.09.008

[43]

Merz B, Kreibich H, Thieken A, Schmidtke R (2004) Estimation uncertainty of direct monetary flood damage to buildings 10.5194/nhess-4-153-2004

[44]

Mishra K, Sinha R (2020) Flood risk assessment in the Kosi megafan using multicriteria decision analysis: a hydro-geomorphic approach. Geomorphology 350:106861. https://doi.org/10.1016/j.geomorph.2019.106861 10.1016/j.geomorph.2019.106861

[45]

Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India

Rajib Mitra, Piu Saha, Jayanta Das

Geomatics, Natural Hazards and Risk 2022 10.1080/19475705.2022.2112094

[46]

Moghadas M, Asadzadeh A, Vafeidis A et al (2019) A multicriteria approach for assessing urban flood resilience in Tehran, Iran. Int J Disaster Risk Reduct 35:101069. https://doi.org/10.1016/J.IJDRR.2019.101069 10.1016/j.ijdrr.2019.101069

[47]

Mohamed SA (2019) Application of satellite image processing and GIS-Spatial modeling for mapping urban areas prone to flash floods in Qena governorate, Egypt. J Afr Earth Sci 158:103507. https://doi.org/10.1016/j.jafrearsci.2019.05.015 10.1016/j.jafrearsci.2019.05.015

[48]

Mohanty PK, Panda US, Pal SR, Mishra P (2008) Monitoring and management of environmental changes along the Orissa Coast. J Coast Res 24:13–27. https://doi.org/10.2112/04-0255.1 10.2112/04-0255.1

[49]

Nam WH, Hayes MJ, Svoboda MD et al (2015) Drought hazard assessment in the context of climate change for South Korea. Agric Water Manag 160:106–117. https://doi.org/10.1016/j.agwat.2015.06.029 10.1016/j.agwat.2015.06.029

[50]

Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol (amst) 513:283–300. https://doi.org/10.1016/j.jhydrol.2014.02.053 10.1016/j.jhydrol.2014.02.053

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Published: Aug 01, 2023
Vol/Issue: 82(16)
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Aditya Kumar Anand

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Sarada Prasad Pradhan

Cite This Article

Aditya Kumar Anand, Sarada Prasad Pradhan (2023). Evaluation of bivariate statistical and hybrid models for the preparation of flood hazard susceptibility maps in the Brahmani River Basin, India. Environmental Earth Sciences, 82(16). https://doi.org/10.1007/s12665-023-11069-w

Evaluation of bivariate statistical and hybrid models for the preparation of flood hazard susceptibility maps in the Brahmani River Basin, India

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