Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods

References

• Aghdam IN, Varzandeh MHM, Pradhan B. 2016. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci. 75:1–20.
   Google Scholar
• Anjum MN, Ding Y, Shangguan D, Ijaz MW, Zhang S. 2016. Evaluation of high-resolution satellite-based real-time and post-real-time precipitation estimates during 2010 extreme flood event in Swat River Basin, Hindukush region. Adv Meteorol. 2016:1–8.
   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. Geomo. 65:15–31.
   Google Scholar
• Ayalew L, Yamagishi H, Ugawa N. 2004. Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides. 1:73–81.
   Google Scholar
• Bai S, Wang J, Zhang Z, Cheng C. 2012. Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena. 99:18–25.
   Google Scholar
• Bohensky EL, Leitch AM. 2014. Framing the flood: a media analysis of themes of resilience in the 2011 Brisbane flood. Reg Environ Change. 14:475–488.
   Google Scholar
• Bourenane H, Bouhadad Y, Guettouche MS, Braham M. 2015. GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Environ.74:337–355.
   Google Scholar
• Bronstert A. 2003. Floods and climate change: interactions and impacts. Risk Anal. 23:545–557.
   Google Scholar
• Bui DT, Lofman O, Revhaug I, Dick O. 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards. 59:1413.
   Google Scholar
• Bui DT, Pradhan B, Nampak H, Bui Q-T, Tran Q-A, Nguyen Q-P. 2016. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS. J Hydrol. 540:317–330.
   Google Scholar
• Chang F-J, Tsai M-J. 2016. A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques. J Hydrol. 535:256–269.
   Google Scholar
• Chau K, Wu C, Li Y. 2005. Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng. 10:485–491.
   Google Scholar
• Chen C-Y, Yu F-C. 2011. Morphometric analysis of debris flows and their source areas using GIS. Geomo. 129:387–397.
   Google Scholar
• Chen SH, Lin YH, Chang LC, Chang FJ. 2006. The strategy of building a flood forecast model by neuro‐fuzzy network. Hydrol process. 20:1525–1540.
   Google Scholar
• Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H. 2016. A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab J Geosci. 9:1–16.
   Google Scholar
• Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C. 2018a. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ. 634:853–867.
   Google Scholar
• Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu A-X. 2018b. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena. 164:135–149.
   Google Scholar
• Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H. 2017. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Haz Risk. 8:950–973.
   Google Scholar
• Chen Y-R, Yeh C-H, 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. 59:1261–1276.
   Google Scholar
• Cinderby S, Forrester JM. 2016. Co-designing possible flooding solutions: participatory mapping methods to identify flood management options from a UK borders case study. J Geogr Inf Sci. 1:149–156.
   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. 76:302.
   Google Scholar
• Dang ATN, Kumar L. 2017. Application of remote sensing and GIS-based hydrological modelling for flood risk analysis: a case study of District 8, Ho Chi Minh City, Vietnam. Geomat Nat Haz Risk. 8:1792–1811.
   Google Scholar
• Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL. 2006. Flood estimation at ungauged sites using artificial neural networks. J Hydrol. 319:391–409.
   Google Scholar
• De Risi R. 2013. A probabilistic bi-scale framework for urban flood risk assessment. Naples (Italy): University of Naples Federico II.
   Google Scholar
• Dempster AP. 2008. A generalization of Bayesian inference. In Kacprzyk J, editor. Classic works of the Dempster-Shafer theory of belief functions. Warsaw (Poland): Springer. p. 73–104.
   Google Scholar
• Fathzadeh A, Jaydari A, Taghizadeh-Mehrjardi R. 2017. Comparison of different methods for reconstruction of instantaneous peak flow data. Intell Autom Soft Co. 23:41–49.
   Google Scholar
• Forte F, Strobl R, Pennetta L. 2006. A methodology using GIS, aerial photos and remote sensing for loss estimation and flood vulnerability analysis in the Supersano-Ruffano-Nociglia Graben, southern Italy. Environ Geol. 50:581–594.
   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.
   Google Scholar
• Gómez-Palacios D, Torres MA, Reinoso E. 2017. Flood mapping through principal component analysis of multitemporal satellite imagery considering the alteration of water spectral properties due to turbidity conditions. Geomat Nat Haz Risk. 8:607–623.
   Google Scholar
• Güçlü YS, Şen Z. 2016. Hydrograph estimation with fuzzy chain model. J Hydrol. 538:587–597.
   Google Scholar
• Hong Y-ST, White PA. 2009. Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm. Adv Water Resour. 32:110–119.
   Google Scholar
• Hostache R, Chini M, Matgen P, Giustarini L. 2013. A new automatic SAR-based flood mapping application hosted on the European Space Agency’s grid processing on demand fast access to imagery environment. Proc EGU General Assembly Conference Abstracts. 15:1–5.
   Google Scholar
• Jaafari A, Najafi A, Pourghasemi H, 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. 11:909–926.
   Google Scholar
• Jayakrishnan R, Srinivasan R, Santhi C, Arnold J. 2005. Advances in the application of the SWAT model for water resources management. Hydrol Process. 19:749–762.
   Google Scholar
• Jebur MN, Pradhan B, Tehrany MS. 2014. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ. 152:150–165.
   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 5:1946–1962.
   Google Scholar
• Juarez-Lucas AM, Kibler KM, Ohara M, Sayama T. 2016. Benefits of flood-prone land use and the role of coping capacity, Candaba floodplains, Philippines. Nat Hazards. 84:2243–2264.
   Google Scholar
• Kang P, Zhang L. 2016. Logistic regression modeling for the length of stay among the hospitalized patients after the 2010 Yushu earthquake. In: Zhang L, editor. Modeling the injury flow and treatment after major earthquakes. Dordrecht: Springer. p. 41–56.
   Google Scholar
• Karimi F, Sultana S, Shirzadi Babakan A, Royall D. 2018. Land suitability evaluation for organic agriculture of wheat using GIS and multicriteria analysis. Appl Geogr. 10:1–17.
   Google Scholar
• Kassa BA. 2014. Assessing the main causes of flood hazard through examining Gis and Rs based Land Use Land Cover (Lulc) change, topography (slope) and drainage density analysis in Alamata Woreda, Southern Tigray Zone, Ethiopia. AJSSH. 4:270–290.
   Google Scholar
• 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 multi-criteria decision-making technique. Nat Hazards. 83:1–41.
   Google Scholar
• Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Bui DT. 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ. 627:744–755.
   Google Scholar
• Khosrokhani M, Pradhan B. 2014. Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS. Geomat Nat Haz Risk. 5:252–270.
   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.
   Google Scholar
• Kim KD, Lee S, Oh HJ, Choi JK, Won JS. 2006. Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS. Environ Geol. 50:1183–1191.
   Google Scholar
• Kjeldsen TR. 2010. Modelling the impact of urbanization on flood frequency relationships in the UK. Hydrol Res. 41:391–405.
   Google Scholar
• Lee M-J, Kang J-e, Jeon S. 2012. Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS), Jul 22–27; Munich (Germany): IEEE International. p. 4.
   Google Scholar
• Lee S. 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens. 26:1477–1491.
   Google Scholar
• Lee S, Pradhan B. 2006. Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Sys Sci. 115:661–672.
   Google Scholar
• Li L, Lan H, Guo C, Zhang Y, Li Q, Wu Y. 2016. A modified frequency ratio method for landslide susceptibility assessment. Landslides. 14:727–741.
   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.
   Google Scholar
• Liong SY, Sivapragasam C. 2002. Flood stage forecasting with support vector machines. Singapore: Wiley Online Library.
   Google Scholar
• Liu Y, De Smedt F. 2004. WetSpa extension, a GIS-based hydrologic model for flood prediction and watershed management. Brussel (Belgium):Vrije Universiteit Brussel. p. 1–108.
   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 Modell Softw. 15:101–124.
   Google Scholar
• Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF. 2014. Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci. 7:711–724.
   Google Scholar
• Mathew J, Jha V, Rawat G. 2009. Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides. 6:17–26.
   Google Scholar
• Merz B, Thieken A, Gocht M. 2007. Flood risk mapping at the local scale: concepts and challenges. In: Begum S, Stive MJF, Hall JW, editors. Flood risk management in Europe Unital. Dordrecht (Nether Land): Springer. p. 231–251.
   Google Scholar
• Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AH. 2017. Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomat Nat Haz Risk. 8:1080–1102.
   Google Scholar
• Nandi A, Mandal A, Wilson M, Smith D. 2016. Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ Earth Sci. 75:1–16.
   Google Scholar
• Nefeslioglu H, Sezer E, Gokceoglu C, Bozkir A, Duman T. 2010. Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl Eng. 1:1–15.
   Google Scholar
• Novelo-Casanova DA, Rodríguez-Vangort F. 2016. Flood risk assessment. Case of study: Motozintla de Mendoza, Chiapas, Mexico. Geomat Nat Haz Risk. 7:1538–1556.
   Google Scholar
• Nurmohamed R, Naipal S, De Smedt F. 2012. Hydrologic modeling of the Upper Suriname River basin using WetSpa and ArcView GIS. JOSH. 6:1–17.
   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.
   Google Scholar
• Pradhan B. 2009. Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Open Geosci. 1:120–129.
   Google Scholar
• Pradhan B. 2010. Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. JOSH. 9:1–18.
   Google Scholar
• Pradhan B, Tehrany MS, Jebur MN. 2016. A new semiautomated detection mapping of flood extent from TerraSAR-X satellite image using rule-based classification and Taguchi optimization techniques. IEEE Trans Geosci Remote Sens. 54:4331–4342.
   Google Scholar
• Pradhan B, Youssef A. 2011. A 100‐year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. J Flood Risk Manag. 4:189–202.
   Google Scholar
• Queensland Floods Commission of Inquiry (QFCI) (2012). Queensland Floods Commission of Inquiry: Final Report. Inquiry, QFCo, & Holmes, CE.
   Google Scholar
• Rahmati O, Pourghasemi HR, Zeinivand H. 2016. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int. 31:42–70.
   Google Scholar
• Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A. 2014. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. 7:725–742.
   Google Scholar
• Shabani F, Kumar L, Esmaeili A. 2014. Improvement to the prediction of the USLE K factor. Geomorphology. 204:229–234.
   Google Scholar
• Shabani F, Shafapour TM, Solhjouy-Fard S, Kumar L. 2018. A comparative modeling study on non-climatic and climatic risk assessment on Asian Tiger Mosquito (Aedes albopictus). Peer J. 6:e4474. doi: 10.7717/peerj.4474.
   Google Scholar
• Sahoo G, Schladow S, Reuter J. 2009. Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. J Hydrol. 378:325–342.
   Google Scholar
• Shafer G. 1976. A mathematical theory of evidence. New Jersey (US): Princeton University Press.
   Google Scholar
• Shrestha RR, Nestmann F. 2009. Physically based and data-driven models and propagation of input uncertainties in river flood prediction. J Hydrol Eng. 14:1309–1319.
   Google Scholar
• Stefanidis S, Stathis D. 2013. Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards. 68:569–585.
   Google Scholar
• Sun D, Yu Y, Goldberg MD. 2011. Deriving water fraction and flood maps from MODIS images using a decision tree approach. J Sel Topics Appl Earth Observ Remote Sens. 4:814–825.
   Google Scholar
• Tehrany M, Jones S. 2017. Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extent of the flood inventory. ISPRS-Int Arc Photogramm, Remote Sens Spatial Inform Sci. 12:209–214.
   Google Scholar
• Tehrany MS, Lee M-J, Pradhan B, Jebur MN, Lee S. 2014a. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci. 72:4001–4015.
   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.
   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.
   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 Environmen Res Risk Assess. 29:1149–1165.
   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.
   Google Scholar
• Tehrany MS, Shabani F, Javier DN and Kumar L. 2017. Soil erosion susceptibility mapping for current and 2100 climate conditions using evidential belief function and frequency ratio. Geomatics Natural Hazards and Risk. 1–21.
   Google Scholar
• Tehrany MS, 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. 8:1538–1561.
   Google Scholar
• Tien Bui D, Le K-TT, Nguyen VC, Le HD, Revhaug I. 2016. Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-Based kernel logistic regression. Remote Sens. 8:347.
   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.
   Google Scholar
• Werner M, Hunter N, Bates P. 2005. Identifiability of distributed floodplain roughness values in flood extent estimation. J Hydrol. 314:139–157.
   Google Scholar
• Xiong Y, Zuo R. 2018. GIS-based rare events logistic regression for mineral prospectivity mapping. Comput Geosci. 111:18–25.
   Google Scholar
• Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena. 72:1–12.
   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.
   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. 7:149–161.
   Google Scholar
• Yusoff S, Pradhan B, Manap MA, Shafri HZM. 2015. Regional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS. Open Geosci. 7:149–161.
   Google Scholar
• Zhang M, Cao X, Peng L, Niu R. 2016. Landslide susceptibility mapping based on global and local logistic regression models in Three Gorges Reservoir area, China. Environ Earth Sci. 75:1–11.
   Google Scholar