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Flood susceptibility mapping using integrated bivariate and multivariate statistical models

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Abstract

Flooding can have catastrophic effects on human lives and livelihoods and thus comprehensive flood management is needed. Such management requires information on the hydrologic, geotechnical, environmental, social, and economic aspects of flooding. The number of flood events that took place in Busan, South Korea, in 2009 exceeded the normal situation for that city. Mapping the susceptible areas helps us to understand flood trends and can aid in appropriate planning and flood prevention. In this study, a combination of bivariate probability analysis and multivariate logistic regression was used to produce flood susceptibility maps of Busan City. The main aim of this research was to overcome the weakness of logistic regression regarding bivariate probability capabilities. A flood inventory map with a total of 160 flood locations was extracted from various sources. Then, the flood inventory was randomly split into a testing dataset 70 % for training the models and the remaining 30 %, which was used for validation. Independent variables datasets included the rainfall, digital elevation model, slope, curvature, geology, green farmland, rivers, slope, soil drainage, soil effect, soil texture, stream power index, timber age, timber density, timber diameter, and timber type. The impact of each independent variable on flooding was evaluated by analyzing each independent variable with the dependent flood layer. The validation dataset, which was not used for model generation, was used to evaluate the flood susceptibility map using the prediction rate method. The results of the accuracy assessment showed a success rate of 92.7 % and a prediction rate of 82.3 %.

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References

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Skianis GA, Chousianitis KG (2013) Assessment of rural community and agricultural development using geomorphological-geological factors and GIS in the Trikala prefecture (Central Greece). Stoch Enviorn Res Risk A 27:573–588

    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. Disaster Prev Manage 15:233–240

    Article  Google Scholar 

  • Campolo M, Soldati A, Andreussi P (2003) Artificial neural network approach to flood forecasting in the River Arno. Hydrolog Sci J 48:381–398

    Article  Google Scholar 

  • Chae EH, Kim TW, Rhee SJ, Henderson TD (2005) The impact of flooding on the mental health of affected people in South Korea. Community Ment Health J 41:633–645

    Article  Google Scholar 

  • Chang H, Franczyk J, Kim C (2009) What is responsible for increasing flood risks? The case of Gangwon Province, Korea. Nat Hazards 48:339–354

    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 

  • 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 

  • De Moel H, Aerts J (2011) Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates. Nat Hazards 58:407–425

    Article  Google Scholar 

  • Dixon B (2005) Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrol 309:17–38

    Article  Google Scholar 

  • Elbialy S, Mahmoud A, Pradhan B, Buchroithner M (2013) Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at Gottleuba Catchment, Saxony, Germany. J Flood Risk Manag. doi:10.1111/jfr3.12037

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

    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 

  • Karjalainen M, Kankare V, Vastaranta M, Holopainen M, Hyyppä J (2012) Prediction of plot-level forest variables using TerraSAR-X stereo SAR data. Remote Sens Environ 117:338–347

    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 67:251–264

    Article  Google Scholar 

  • Kjeldsen TR (2010) Modelling the impact of urbanization on flood frequency relationships in the UK. Hydrol Res 41:391–405

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855

    Article  Google Scholar 

  • Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990

    Article  Google Scholar 

  • Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338

    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, 895–898

  • Liu Y, De Smedt F (2005) Flood modeling for complex terrain using GIS and remote sensed information. Water Resour Manag 19:605–624

    Article  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  • Martinez JM, Le Toan T (2007) Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens Environ 108:209–223

    Article  Google Scholar 

  • Mason DC, Speck R, Devereux B, Schumann GP, Neal JC, Bates PD (2010) Flood detection in urban areas using TerraSAR-X. IEEE T Geosci Remote 48:882–894

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Okamoto K, Yamakawa S, Kawashima H (1998) Estimation of flood damage to rice production in North Korea in 1995. Int J Remote Sens 19:365–371

    Article  Google Scholar 

  • Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou HD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Eng Geol 158:77–88

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464

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

    Google Scholar 

  • Pradhan B (2010b) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote 38:301–320

    Article  Google Scholar 

  • Pradhan B, Lee S (2010a) 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, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759

    Article  Google Scholar 

  • Pradhan B, Lee S (2010c) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

    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 TerraSAR-X satellite image. Comput Geosci 63:34–43

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Dhital MR, Pradhan B (2013) Weathering and mineralogical variation in gneissic rocks and their effect in Sangrumba Landslide, East Nepal. Environ Earth Sci 71:1–17

    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 

  • Sanyal J, Lu X (2004) Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat Hazards 33:283–301

    Article  Google Scholar 

  • Skilodimou H, Livaditis G, Bathrellos G, Verikiou-Papaspiridakou E (2003) Investigating the flooding events of the urban regions of Glyfada and Voula, Attica, Greece: a contribution to Urban Geomorphology. Geogr Ann A 85:197–204

    Article  Google Scholar 

  • Subramanian N, Ramanathan R (2012) A review of applications of analytic hierarchy process in operations management. Int J Prod Econ 138:215–241

    Article  Google Scholar 

  • Taylor J, Davies M, Clifton D, Ridley I, Biddulph P (2011) Flood management: prediction of microbial contamination in large-scale floods in urban environments. Environ Int 37:1019–1029

    Article  Google Scholar 

  • 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

    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. doi:10.1016/j.jhydrol.2014.03.008

  • 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 

  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118:124–135

    Article  Google Scholar 

  • Yi CS, Lee JH, Shim MP (2010) GIS-based distributed technique for assessing economic loss from flood damage: pre-feasibility study for the Anyang Stream Basin in Korea. Nat Hazards 55:251–272

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Zwenzner H, Voigt S (2009) Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrol Earth Syst Sci 13:567–576

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) and by the Development Project of Environmental Technology for Climate Change by the Korea Environmental Industry & Technology Institute. Thanks to anonymous reviewers for their valuable feedback on the manuscript.

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Correspondence to Saro Lee.

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Tehrany, M.S., Lee, MJ., Pradhan, B. et al. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72, 4001–4015 (2014). https://doi.org/10.1007/s12665-014-3289-3

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  • DOI: https://doi.org/10.1007/s12665-014-3289-3

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