Advertisement

Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 395–408 | Cite as

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

  • Ratan Kumar Samanta
  • Gouri Sankar Bhunia
  • Pravat Kumar Shit
  • Hamid Reza Pourghasemi
Original 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.

Keywords

Flood susceptibility Frequency ratio Bivariate statistics Remote sensing GIS 

Notes

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.

Compliance with ethical standards

Conflict of interest

No conflict of interest was reported by the authors.

References

  1. 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 1:1–17Google Scholar
  2. 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):39–45CrossRefGoogle Scholar
  3. Bubeck P, Botzen W, Aerts J (2012) A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal 32:1481–1495CrossRefGoogle Scholar
  4. Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423CrossRefGoogle Scholar
  5. 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):1261–1276CrossRefGoogle Scholar
  6. 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 27:1–17CrossRefGoogle Scholar
  7. 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):791–797Google Scholar
  8. 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
  9. 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
  10. 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 67:2205–2215CrossRefGoogle Scholar
  11. 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):377–387CrossRefGoogle Scholar
  12. 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 27:535–548CrossRefGoogle Scholar
  13. Imrie C, Durucan S, Korre A (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range. J Hydrol 233(1):138–153CrossRefGoogle Scholar
  14. Irrigation & Waterways Department (2011) Annual Flood Report, Government of West Bengal. http://www.wbiwd.gov.in/index.php/applications/anual_flood_report
  15. 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 CrossRefGoogle Scholar
  16. 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):251–264CrossRefGoogle Scholar
  17. 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):457–474CrossRefGoogle Scholar
  18. 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):212–225Google Scholar
  19. 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
  20. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41CrossRefGoogle Scholar
  21. 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 895–898Google Scholar
  22. 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 356–360:2372–2375CrossRefGoogle Scholar
  23. 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 47:1454–1466CrossRefGoogle Scholar
  24. Lohani A, Kumar R, Singh R (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442:23–35CrossRefGoogle Scholar
  25. Ma ZM (2005) Engineering information modeling in databases: needs and constructions. Ind Manage Data Sys 105 (7):900-918CrossRefGoogle Scholar
  26. Manandhar B (2010) Flood plain analysis and risk assessment of Lothar Khola, Nepal: Unpublished Ph.D. thesis, Tribhuvan University, Nepal.Google Scholar
  27. 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 2:59CrossRefGoogle Scholar
  28. Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 14(6):647–652CrossRefGoogle Scholar
  29. 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 CrossRefGoogle Scholar
  30. 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 2:57CrossRefGoogle Scholar
  31. 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 63:965–996CrossRefGoogle Scholar
  32. Pradhan B (2009) Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci 1:120–129Google Scholar
  33. 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 60:1037–1054CrossRefGoogle Scholar
  34. Pradhan B (2010) Flood susceptible mapping and risk area estimation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2):2–12Google Scholar
  35. 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):107–126CrossRefGoogle Scholar
  36. 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 1:199–223CrossRefGoogle Scholar
  37. 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 63:34–43CrossRefGoogle Scholar
  38. 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
  39. 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
  40. 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
  41. Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187CrossRefGoogle Scholar
  42. 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 63:49–63CrossRefGoogle Scholar
  43. 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.Google Scholar
  44. 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 CrossRefGoogle Scholar
  45. 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):8208–8219CrossRefGoogle Scholar
  46. Shabani F, Kumar L, Esmaeili A (2014) Improvement to the prediction of the USLE K factor. Geomorphology 204:229–234CrossRefGoogle Scholar
  47. 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 7:702–720CrossRefGoogle Scholar
  48. 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)Google Scholar
  49. 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):7456–7468CrossRefGoogle Scholar
  50. 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 512:332–343CrossRefGoogle Scholar
  51. 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 CrossRefGoogle Scholar
  52. Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015b) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101CrossRefGoogle Scholar
  53. 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 CrossRefGoogle Scholar
  54. 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 45:199–211CrossRefGoogle Scholar
  55. Tiwari MK, Chatterjee C (2010) Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). J Hydrol 382(1):20–33CrossRefGoogle Scholar
  56. 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 18:2343–2357CrossRefGoogle Scholar
  57. 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 2Google Scholar
  58. 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 62:611–623CrossRefGoogle Scholar
  59. 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
  60. 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):525–546CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ratan Kumar Samanta
    • 1
  • Gouri Sankar Bhunia
    • 2
  • Pravat Kumar Shit
    • 3
  • Hamid Reza Pourghasemi
    • 4
  1. 1.Department of GeographySubarnarekha MahavidyalayaGopiballavpurIndia
  2. 2.Aarvee Associates Architects, Engineers & Consultants Pvt LtdHyderabadIndia
  3. 3.Department of GeographyRaja N.L.Khan Women’s CollegeMedinipurIndia
  4. 4.Department of Natural Resources and Environmental Engineering, College of AgricultureShiraz UniversityShirazIran

Personalised recommendations