Skip to main content

Advertisement

SpringerLink
Log in

GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India

  • Original Paper
  • Published:
International Journal of Environmental Science and Technology Aims and scope Submit manuscript

Abstract

The prime purpose of this study is to explore the use of RS and GIS for flood risk mapping in the Patna district in conjunction with the frequency ratio and entropy model. The variables such as elevation, LULC, rainfall, slope, distance from the river, TWI, and drainage density have played significant roles in causing floods in the study area. The flood vulnerability map has been generated and categorized as very high, high, moderate, low, and very low vulnerable flood areas. The validation of the results outcomes was conducted using the ROC curve, which indicates that the frequency ratio model for flood vulnerability mapping is more authentic, reliable and can assist in demarcating flood vulnerable areas and potential flood risk sites. The results show that about 16.35% (FR model) and 9.98% (SEI model) of the study area have come under very high risk of flooding, especially located in the southeast and northwestern sites of the district. The blockwise flood risk assessment shows that 13 out of 23 blocks recorded over 25% of their total area under very high flood risk. The present study reveals the usefulness of RS and GIS techniques along with bivariate models for the investigation and evaluation of flood vulnerabilities as they provide a wide range of data and reliable platforms. The results of this report will assist planners in developing flood prevention plans as part of regional flood risk management initiatives and will serve as a foundation for future studies in the study region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

CHRS:

Centre for Hydrometeorology and Remote Sensing

CWC:

Central Water Commission

IMD:

Indian Meteorological Department

NRSC:

National Remote Sensing Centre

WMO:

World Meteorological Organization

RS:

Remote sensing

GIS:

Geographic information system

BSA:

Bivariate statistical analysis

FR:

Frequency ratio

SEI:

Shannon’s entropy index

FVM:

Flood vulnerability map

DEM:

Digital elevation model

SPI:

Stream power index

TWI:

Topographic wetness index

LULC:

Land use land cover

NDVI:

Normalized difference vegetation index

ROC:

Receiver operating characteristic

AUC:

Area under the ROC curve

References

  • Adiat KAN, Nawawi MNM, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool—a case of predicting potential zones of sustainable groundwater resources. J Hydrol 440:75–89

    Article  Google Scholar 

  • 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(9):1–19

    Article  Google Scholar 

  • Al-Hinai HY, Abdalla R (2020) Spatial prediction of coastal flood-susceptible areas in Muscat governorate using an entropy weighted method. WIT Trans Eng Sci 129:121–133

    Article  Google Scholar 

  • 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 Geoinf 10(4):252

    Article  Google Scholar 

  • 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(3):1083–1102

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J 24(1):43–69

    Article  Google Scholar 

  • Billa L, Shattri M, Mahmud AR, Ghazali AH (2006) Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disast Prev Manag Int J

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Comput Methods Geosci 13:398

    Google Scholar 

  • Boyle SJ, Tsanis IK, Kanaroglou PS (1998) Developing geographic information systems for land use impact assessment in flooding condition. J Wat Resour Plan Manag ASCE 124(2):89–98

    Article  Google Scholar 

  • Central Water Commission (CWC) (2010) Water and related statistics, Water Resource Information System Directorate, New Delhi, pp 198–247

  • Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Ahmad BB (2020) Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Sci Tot Environ 701:134979

    Article  CAS  Google Scholar 

  • 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 Spa Res 65(5):1466–1489

    Article  Google Scholar 

  • Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375(3–4):613–626

    Article  Google Scholar 

  • Costache R, Popa MC, Bui DT, Diaconu DC, Ciubotaru N, Minea G, Pham QB (2020) Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. J Hydrol 585:124808

    Article  Google Scholar 

  • Dahri N, Abida H (2017) Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia). Environ Earth Sci 76(7):302

    Article  Google Scholar 

  • Dang NM, Babel MS, Luong HT (2011) Evaluation of food risk parameters in the day river flood diversion area, Red River delta. Vietnam Nat Hazards 56(1):169–194

    Article  Google Scholar 

  • Dhar ON, Nandargi S (2003) Hydrometeorological aspects of floods in India. Nat Hazards 28(1):1–33

    Article  Google Scholar 

  • Dhar ON, Rakhecha PR, Mandal BN, Sangam RB (1981b) The rainstorm which caused the Morvi dam disaster in August 1979/L’orage qui a provoqué la catastrophe du barrage Morvi août 1979. Hydrol Sci J 26(1):71–81

    Article  Google Scholar 

  • Dhar ON, Mandal BN, Ghose GC (1981a) Vamsadhara flash flood of September 1980—a brief appraisal

  • Dottori F, Martina MLV, Figueiredo R (2018) A methodology for flood susceptibility and vulnerability analysis in complex flood scenarios. J Flo Ri Man 11:S632–S645

    Google Scholar 

  • Du J, Fang J, Xu W, Shi P (2013) Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stoch Environ Res Risk Assess 27(2):377–387

    Article  Google Scholar 

  • Esteves LS (2013) Consequences to flood management of using different probability distributions to estimate extreme rainfall. J Environ Manag 115:98–105

    Article  Google Scholar 

  • Feng CC, Wang YC (2011) GIScience research challenges for emergency management in Southeast Asia. Nat Hazards 59(1):597–616

    Article  Google Scholar 

  • Fernández DS, Lutz MA (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98

    Article  Google Scholar 

  • Ghoneim E, Foody GM (2013) Assessing flash flood hazard in an arid mountainous region. Arab J Geosci 6(4):1191–1202

    Article  Google Scholar 

  • Glenn E, Morino K, Nagler P, Murray R, Pearlstein S, Hultine K (2012) Roles of saltcedar (Tamarix spp) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J Arid Environ 79:56–65

    Article  Google Scholar 

  • Goswami DC (1998) Fluvial regime and flood hydrology of the Brahmaputra River, Assam. Memoir Geol Soc India 41:53–75

    Google Scholar 

  • Greenbaum D (1989) Hydrogeological applications of remote sensing in areas of crystalline basement. In: Proceedings of groundwater exploration and development in crystalline basement Aquifers, Zimbabwe

  • Gül GO (2013) Estimating flood exposure potentials in Turkish catchments through index-based flood mapping. Nat Hazards 69:403–423

    Article  Google Scholar 

  • Gupta S, Javed A, Dutt D (2003) Economics of flood protection in India. Nat Hazards 28:199–210

    Article  Google Scholar 

  • Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126(3):39

    Article  Google Scholar 

  • Hirabayashi Y, Kanae S (2009) First estimate of the future global population at risk of flooding. Hydrol Res Lett 3:6–9

    Article  Google Scholar 

  • Hong H, Tsangaratos P, Ilia I, Liu J, Zhu AX, Chen W (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci Environ 625:575–588

    CAS  Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926

    Article  Google Scholar 

  • Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Lee S (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the tafresh watershed, Iran. Sustainability 11(19):5426

    Article  Google Scholar 

  • Jebur MN, Pradhan B, Tehrany MS (2015) Using ALOS PALSAR derived high-resolution DInSAR to detect slow-moving landslides in tropical forest, Cameron Highlands, Malaysia. J Geom Nat Hazards Risk 8:1–19. https://doi.org/10.1080/194757052013860407

    Article  Google Scholar 

  • Jonkman SN, Kelman I (2005) An analysis of the causes and circumstances of flood disaster deaths. Disasters 29(1):75–97

    Article  Google Scholar 

  • Jothibasu A, 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

    Google Scholar 

  • Kashif U, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. PLoS ONE 15(3):e0229153

    Article  CAS  Google Scholar 

  • Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurements 131:35–41

    Google Scholar 

  • 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(2):947–987

    Article  Google Scholar 

  • 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(12):1–21

    Article  Google Scholar 

  • 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(1):251–264

    Article  Google Scholar 

  • Kourgialas NN, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas: a case study. Hydrol Sci J 56:212–225

    Article  Google Scholar 

  • Lee MJ, Kang JE, Jeon S (2012) Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In: 2012 IEEE international geoscience remote sensing symposium, pp 895–898

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41

    Article  Google Scholar 

  • Li XH, Zhang Q, Shao M, Li YL (2012) A comparison of parameter estimation for distributed hydrological modelling using automatic and manual methods. J Adv Mater Res 356:2372–2375

    Article  Google Scholar 

  • Liao X, Carin L (2009) Migratory logistic regression for learning concept drift between two data sets with application to UXO sensing. IEEE Trans Geosci Remote Sens 47(5):1454–1466

    Article  Google Scholar 

  • Mangat HS (1994) Patiala floods: where lies the solution. Trans Inst Indi Geogra 16(1):29–33

    Google Scholar 

  • Merz B, Kreibich H, Thieken A, Schmidtke R (2004) Estimation uncertainty of direct monetary flood damage to buildings. Nat Hazards Earth Syst Sci 4(1):153–163

    Article  Google Scholar 

  • Merz B, Thieken AH, Gocht M (2007) Flood risk mapping at the local scale: concepts and challenges. J Flood Risk Manag 231–251

  • Messner F, Meyer V (2006) Flood damage, vulnerability and risk perception–challenges for flood damage research In: Flood risk management: hazards, vulnerability and mitigation measures. Springer, Dordrecht, pp 149–167

  • Moghaddam DD, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2015) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed. Iran Ara J Geosci 8(2):913–929

    Article  Google Scholar 

  • Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AHB (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomat Nat Haz Risk 8(2):1080–1102

    Article  Google Scholar 

  • Mondal S, Maiti R (2013) Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya. Int J Dis Ris Sci 4(4):200–212

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300

    Article  Google Scholar 

  • Nandi A, Mandal A, Wilson M, Smith D (2016) Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Envir Ear Sci 75(6):465

    Article  Google Scholar 

  • Ohl CA, Tapsell S (2000) Flooding and human health: the dangers posed are not always obvious

  • Ouma YO, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water 6(6):1515–1545

    Article  Google Scholar 

  • Parthasarathy B, Sontakke NA, Monot AA, Kothawale DR (1987) Droughts/floods in the summer monsoon season over different meteorological sub-divisions of india for the period 1871–1982. J Climat 7:5770

    Article  Google Scholar 

  • Paudyal GN (1996) An integrated GIS-numerical modelling system for advanced flood management. In: Proceedings of international conference on water resources & environment research: towards the 21st century. Kyoto University, Japan, pp 555–562

  • Pham BT, Jaafari A, Van Phong T, Yen HPH, Tuyen TT, Van Luong V, Foong LK (2021) Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci Front 12(3):101105

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan MM (2012) Landslide susceptibility mapping using index of entropy and conditional probability models at Safarood Basin, Iran. CATENA 97:71–84

    Article  Google Scholar 

  • Pradhan B (2010) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9:1–18

    Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2015) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int. https://doi.org/10.1080/10106049.2015.1041559

    Article  Google Scholar 

  • Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016a) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82(2):1231–1258

    Article  Google Scholar 

  • Rahmati O, Haghizadeh A, Stefanidis S (2016b) Assessing the accuracy of GIS-based analytical hierarchy process for watershed prioritization; Gorganrood River Basin. Iran Water Resour Manag 30(3):1131–1150

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016c) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int 31(1):42–70

    Article  Google Scholar 

  • Rahmati O, Zeinivand H, Besharat M (2016d) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Haz Risk 7(3):1000–1017

    Article  Google Scholar 

  • Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187

    Article  Google Scholar 

  • Sabatakakis N, Koukis G, Vassiliades E, Lainas S (2013) Landslide susceptibility zonation in Greece. Nat Hazards 65(1):523–543

    Article  Google Scholar 

  • Saha S, Mondal P (2020) A catastrophic flooding event in north bengal, 2017 and its impact assessment: a case study of raiganj CD Block Uttar Dinajpur, West Bengal. Appl Geosp Tech Geomorp Env IGI Conference. ISBN 978-81-925799-3-1

  • 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

    Article  Google Scholar 

  • Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR (2018a) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4(1):395–408

    Article  Google Scholar 

  • Samanta S, Pal DK, Palsamanta B (2018b) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8(2):1–14

    Article  Google Scholar 

  • Sarhadi A, Soltani S, Modarres R (2012) Probabilistic flood inundation mapping of ungauged rivers: linking GIS techniques and frequency analysis. J Hydrol 458:68–86

    Article  Google Scholar 

  • 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(1):1–13

    Article  CAS  Google Scholar 

  • Sarkar D, Pal S (2018) Construction of avulsion potential zone model for Kulik River of Barind Tract, India and Bangladesh. Environ Monit Assess 190(5):1–17

    Article  Google Scholar 

  • Schober B, Hauer C, Habersack H (2015) A novel assessment of the role of Danube floodplains in flood hazard reduction (FEM method). Nat Hazards 75(1):33–50

    Article  Google Scholar 

  • Shafapour TM, Lee MJ, Pradhan B, Jebur MN, Lee S (2014) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci. https://doi.org/10.1007/s12665-014-3289-3

    Article  Google Scholar 

  • Shafapour TM, Kumar L, Neamah JM, 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(1):79–101

    Article  Google Scholar 

  • Shafapour Tehrany M, Shabani F, Neamah Jebur M, 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. Geomat Nat Haz Risk 8(2):1538–1561

    Article  Google Scholar 

  • Sharif HO, Al-Juaidi FH, Al-Othman A, Al-Dousary I, Fadda E, Jamal US, Elhassan A (2016) Flood hazards in an urbanizing watershed in Riyadh, Saudi Arabia. Geomat Nat Haz Risk 7:702–720

    Article  Google Scholar 

  • Singh O, Kumar M (2013) Flood events, fatalities and damages in India from 1978 to 2006. Nat Hazards 69(3):1815–1834

    Article  Google Scholar 

  • Smith K, Petley DN (2009) Environmental hazard, accessing risk and reducing disaster, 5th edn. Brit Libr. ISBN 10: 0-203-88480-9

  • Smith K, Ward R (1998) Mitigating and managing flood losses floods: physical processes and human impacts. Wiley, Chichester

    Google Scholar 

  • 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. Geoc Intern 35(9):991–1017

    Article  Google Scholar 

  • Swain KC, Singha C, Nayak L (2020) Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS Intel J Ge-Inf 9(12):720

    Article  Google Scholar 

  • Talha S, Maanan M, Atika H, Rhinane H (2019) Prediction of flash flood susceptibility using fuzzy analytical hierarchy process (Fahp) algorithms and Gis: a study case of guelmim region In Southwestern of Morocco. Inter Arch Photo Rem Sen Spa Info Sci 42:407–414

    Google Scholar 

  • Tehrany MS, Lee MJ, Pradhan B, Jebur MN, Lee S (2014a) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72(10):4001–4015

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2014b) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2015a) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Environ Res Risk Assess 29(4):1149–1165

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015b) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125:91–101

    Article  Google Scholar 

  • Tehrany SM, Shabani F, Neamah JM, 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. Geo Nat Hazard Risk 8(2):1538–1561

    Article  Google Scholar 

  • Tehrany M, Kumar L, Neamah JM, Shabani F (2019a) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geo Nat Hazards Risk 10(1):79–101

    Article  Google Scholar 

  • Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11(2):364

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H, Peng L (2020) Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol 582:124482

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H, Costache R, Tang X (2021) Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. J Environ Manag 289:112449

    Article  Google Scholar 

  • Weier J, Herring D (2000) Measuring vegetation (NDVI & EVI). NASA Earth Observatory, Washington

    Google Scholar 

  • WHO (2003) World Health Organization disaster data-key trends and statistics in world disasters report WHO. Switzerland, Geneva

    Google Scholar 

  • Wilson JP, Gallant JC (2000) Terrain analysis: principles and applications. Wiley, New York, p 479

    Google Scholar 

  • WMO (World Meteorological Organization) (1994) On the front-line: public weather service, WMO No 816. WMO, Geneva

    Google Scholar 

  • Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62(3):611–623

    Article  Google Scholar 

  • Yu JJ, Qin XS, Larsen O (2013) Joint Monte Carlo and possibilistic simulation for flood damage assessment. Stoch Environ Res Risk Assess 27(3):725–735

    Article  Google Scholar 

  • Zhao G, Pang B, Xu Z, Peng D, Xu L (2019) Assessment of urban flood susceptibility using semi-supervised machine learning model. Sci Environ 659:940–949

    CAS  Google Scholar 

Download references

Acknowledgements

Our heartfelt appreciation goes to Prof. Dr. Gopal Chandra Debnath (Visva Bharati University), who has expertly led us throughout the whole research, offering encouraging tips, introducing new facets to this work, and patiently covering the whole section and making minute corrections. Authors are also grateful to the reviewers and editors, for their insightful comments and suggestions for the improvement and expansion of the work.

Author information

Authors and Affiliations

  1. Department of Geography, Raiganj University, Raiganj, West Bengal, 733134, India

    D. Sarkar, S. Saha & P. Mondal

Authors
  1. D. Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Mondal.

Ethics declarations

Conflict of interest

The authors hereby declare that there is no conflict of interest and no human or animal involved or harmed in any way during this research work.

Additional information

Editorial resonsibility: Shahid Hussain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sarkar, D., Saha, S. & Mondal, P. GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. Int. J. Environ. Sci. Technol. 19, 8911–8932 (2022). https://doi.org/10.1007/s13762-021-03627-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13762-021-03627-1

Keywords

Search

Navigation