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Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India

Modeling Earth Systems and Environment volume 4, pages 395–408 (2018)Cite this article

Abstract

Flood is one of the most damaging catastrophic natural hazards affecting human lives in India. So, flood susceptibility mapping is essential for urban hydrology management. In this study, a Geographical Information System based-bivariate statistical analysis namely frequency ratio (FR) model was used to assess flood susceptibility of the middle and lower catchments of Subarnarekha River. The flood inventory map was made using field surveys and formal reports in the study area. In general, 32 flood locations (70%) that were inundated in June, 2008 was used for statistical analysis as training dataset, while the remaining 30% (14 flood locations) flooded areas were applied to validate the developed model. Eight flood conditioning factors namely elevation, slope, topographical wetness index, geomorphology, soil type, drainage, rainfall, and LULC (land use/land cover) were considered in this study. All these variables were resampled into 20 × 20 m pixel size. Each variable was classified using the quantile method and the FR probability model was used to evaluate the relationship of each class with flood occurrences. Finally, the flood susceptibility map was prepared and classified into very low, low, moderate, high, and very high susceptibility. Results of the built model was validated with the ground data (30% flood locations) using the area under the curves (AUC). The AUC for success rate was estimated as 84.80%, while the prediction rate was 81.20%. The produced flood susceptibility mapping using FR model could be important for researchers, planners, and local governments in order to flood mitigation strategies.

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References

  • Al-Zahrani M, Al-Areeq A, Sharif HO (2016) Estimating urban flooding potential near the outlet of an arid catchment in Saudi Arabia. Geomat Nat Hazards Risk 1:1–17

    Google Scholar 

  • Bhuyan NK, Sahu B, Rout SP (2014) Assessment of Water Quality Index in Subarnarekha River Basin in and around Jharkhand Area. IOSR J Environ Sci Toxicol Food Technol (IOSR-JESTFT) 8(11):39–45

    Article  Google Scholar 

  • Bubeck P, Botzen W, Aerts J (2012) A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal 32:1481–1495

    Article  Google Scholar 

  • Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423

    Article  Google Scholar 

  • Chen YR, Yeh CH, Yu B (2011) Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat Hazards 59(3):1261–1276

    Article  Google Scholar 

  • Chowdary V, Chakraborthy D, Jeyaram A, Murthy YK, Sharma J, Dadhwal V (2013) Multi-riteria decision making approach for watershed prioritization using analytic hierarchy process technique and GIS. Water Resour Manag 27:1–17

    Article  Google Scholar 

  • Dandapat K, Panda GK (2013) Drainage and floods in the Subarnarekha Basin in Paschim Medinipur, West Bengal, India—a study in applied geomorphology. Int J Sci Res (IJSR) 4(5):791–797

    Google Scholar 

  • Dandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ. https://doi.org/10.1007/s40808-017-0388-7

    Google Scholar 

  • Das B, Bandyopadhyay A (2015) Flood Risk Reduction of Rupnarayana River, towards Disaster Management?A Case Study at Bandar of Ghatal Block in Gangetic Delta. J Geogr Nat Disast 5:135. https://doi.org/10.4172/2167-0587.1000135

    Google Scholar 

  • Dawod GM, Mirza MN, Al-Ghamdi KA (2012) GIS-based estimation of flood hazard impacts on road network in Makkah city, Saudi Arabia. Environ Earth Sci 67:2205–2215

    Article  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 

  • Ghalkhani H, Golian S, Saghafian B, Farokhnia A, Shamseldin A (2013) Application of surrogate artificial intelligent models for real-time flood routing. Water Environ J 27:535–548

    Article  Google Scholar 

  • Imrie C, Durucan S, Korre A (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range. J Hydrol 233(1):138–153

    Article  Google Scholar 

  • Irrigation & Waterways Department (2011) Annual Flood Report, Government of West Bengal. http://www.wbiwd.gov.in/index.php/applications/anual_flood_report

  • Khosravi K, Pourghasemi HR, Chapi K, Bahri M (2016) 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

    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 

  • Kisi O, Nia AM, Gosheh MG, Tajabadi MRJ, Ahmadi A (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 26(2):457–474

    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(2):212–225

  • Kumar N, Lal D, Sherring A, Issac RK (2017) Applicability of HEC-RAS & GFMS tool for 1D water surface elevation/flood modeling of the river: a Case Study of River Yamuna at Allahabad (Sangam), India. Model Earth Syst Environ. https://doi.org/10.1007/s40808-017-0390-0

    Google Scholar 

  • 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 

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

  • Li XH, Zhang Q, Shao M, Li YL (2012) A comparison of parameter estimation for distributed hydrological modelling using automatic and manual methods. Adv Mater Res 356–360:2372–2375

    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 47:1454–1466

    Article  Google Scholar 

  • Lohani A, Kumar R, Singh R (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442:23–35

    Article  Google Scholar 

  • Ma ZM (2005) Engineering information modeling in databases: needs and constructions. Ind Manage Data Sys 105 (7):900-918

    Article  Google Scholar 

  • Manandhar B (2010) Flood plain analysis and risk assessment of Lothar Khola, Nepal: Unpublished Ph.D. thesis, Tribhuvan University, Nepal.

  • Mandal SP, Chakrabarty A (2016) Flash flood risk assessment for upper Teesta river basin: using the hydrological modeling system (HEC-HMS) software. Model Earth Syst Environ 2:59

    Article  Google Scholar 

  • Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 14(6):647–652

    Article  Google Scholar 

  • Pakoksung K, Takagi M (2016) Effect of satellite based rainfall products on river basin responses of runoff simulation on flood event. Model Earth Syst Environ 2:143. https://doi.org/10.1007/s40808-016-0200-0

    Article  Google Scholar 

  • Pal R, Pani P (2016) Seasonality, barrage (Farakka) regulated hydrology and flood scenarios of the Ganga River: a study based on MNDWI and simple Gumbel model. Model Earth Syst Environ 2:57

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996

    Article  Google Scholar 

  • Pradhan B (2009) Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci 1:120–129

    Google Scholar 

  • Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  • Pradhan B (2010) Flood susceptible mapping and risk area estimation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2):2–12

    Google Scholar 

  • Pradhan B, Buchroithner MF (2010) Comparison and Validation of Landslide Susceptibility Maps Using an Artificial Neural Network Model for Three Test Areas in Malaysia. Environ Eng Geosci 16(2):107–126

    Article  Google Scholar 

  • Pradhan B, Oh HJ, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1:199–223

    Article  Google Scholar 

  • Pradhan B, Hagemann U, Tehrany MS, Prechtel N (2014) An easy to use ArcMap based texture analysis program for extraction of flooded areas from Terra SAR-X satellite image. Comput Geosci 63:34–43

    Article  Google Scholar 

  • Prasad E and Mukherjee N (2014) Situation analysis on floods and flood management, ecosystems for life: A Bangladeah-lndia initiative, IUCN, International union for conservation of nature, New Delhi, pp 124. https://cmsdata.iucn.org

  • Prasad RN, Pani P (2017) Geo-hydrological analysis and sub watershed prioritization for flash flood risk using weighted sum model and Snyder’s synthetic unit hydrograph. Model Earth Syst. https://doi.org/10.1007/s40808-017-0354-4

    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 31(1). https://doi.org/10.1080/10106049.2015.1041559

  • 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 

  • Rozos D, Bathrellos GD, Skillodimou HD (2011) Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece. Environ Earth Sci 63:49–63

    Article  Google Scholar 

  • Samanta R K (2013) Land degradation and water resource management problems in the middle and upper catchment of Subarnarekha river in the part of West Bengal and Orissa. Medinipur,: Unpublished Ph.D. thesis, Vidyasagar University, Medinipur.

  • Sar N, Chatterjee S, Adhikari MD (2015) Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India. Model Earth Syst Environ 1:31. https://doi.org/10.1007/s40808-015-0039-9

    Article  Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang Valley Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  • Shabani F, Kumar L, Esmaeili A (2014) Improvement to the prediction of the USLE K factor. Geomorphology 204:229–234

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Shit PK, Nandi AS, Bhunia GS (2015) Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India. Model Earth Syst Environ 1(3)

  • Talei A, Chua LHC, Quek C (2010) A novel application of a neurofuzzy computational technique in event-based rainfall–runoff modeling. Expert Syst Appl 37(12):7456–7468

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2014) 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. https://doi.org/10.1007/s00477-015-1021-9

    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 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. Geomat Nat Hazards Risk 8(2):1538–1561. https://doi.org/10.1080/19475705.2017.1362038

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211

    Article  Google Scholar 

  • Tiwari MK, Chatterjee C (2010) Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). J Hydrol 382(1):20–33

    Article  Google Scholar 

  • Wanders N, Karssenberg D, de Roo A, de Jong SM, Bierkens MFP (2014) The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol Earth Syst Sci 18:2343–2357

    Article  Google Scholar 

  • Youssef AM, Hegab MA (2005) Using geographic information systems and statistics for developing a database management system of the flood hazard for Ras Gharib area, Eastern Desert, Egypt. In: The fourth international conference on the geology of Africa, vol 2

  • 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 62:611–623

    Article  Google Scholar 

  • 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 75:12 (1–16) https://doi.org/10.1007/s12665-015-4830-8

  • Zou Q, Zhou J, Zhou C, Song L, Guo J (2013) Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch Environ Res Risk Assess 27(2):525–546

    Article  Google Scholar 

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Acknowledgements

Authors are thankful to anonymous reviewers and Md. Nazrul Islam, Executive Editor-in-Chief for their constructive comments and suggestions to improve the manuscript. The author (P. K. Shit) grateful acknowledges University Grant Commission (UGC), Govt. of India for financial support through Minor Research Project [No.F.PHW-171/15-16 (ERO)]. We are thankful to Department of Geography and Environment Management, Vidyasagar University, Midnapore, West Bengal, India for providing all necessary support.

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Authors and Affiliations

  1. Department of Geography, Subarnarekha Mahavidyalaya, Gopiballavpur, West Bengal, 721506, India

    Ratan Kumar Samanta

  2. Aarvee Associates Architects, Engineers & Consultants Pvt Ltd, Hyderabad, 500 082, India

    Gouri Sankar Bhunia

  3. Department of Geography, Raja N.L.Khan Women’s College, Gope Palace, Medinipur, West Bengal, 721102, India

    Pravat Kumar Shit

  4. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

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  1. Ratan Kumar Samanta
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  2. Gouri Sankar Bhunia
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  3. Pravat Kumar Shit
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  4. Hamid Reza Pourghasemi
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Correspondence to Pravat Kumar Shit.

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Samanta, R.K., Bhunia, G.S., Shit, P.K. et al. Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model. Earth Syst. Environ. 4, 395–408 (2018). https://doi.org/10.1007/s40808-018-0427-z

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