Skip to main content

Advertisement

SpringerLink
Log in

Flood hazard mapping in Jamaica using principal component analysis and logistic regression

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Jamaica, the third largest island in the Caribbean, has been affected significantly by flooding and flood-related damage. Hence assessing the probability of flooding and susceptibility of a place to flood hazard has become a vital part of planning and development. In addition to heavy rainfall from tropical storms and Atlantic hurricanes, several terrestrial factors play significant roles in flooding, including local geology, geomorphology, hydrology and land-use. In this study, a GIS-based multi-criteria statistical methodology was developed to quantify hazard potential and to map flood characteristics. Fourteen factors potentially responsible for flooding were identified and used as initial input in a hybrid model that combined principal component analysis with logistic regression and frequency distribution analysis. Of these factors, seven explained 65 % of the variation in the data: elevation, slope angle, slope aspect, flow accumulation, a topographic wetness index, proximity to a stream network, and hydro-stratigraphic units. These were used to prepare the island’s first map of flood hazard potential. Hazard potential was classified from very low to very high, nearly one-fifth (19.4 %) of the island was included within high or very high flood hazard zones. Further analysis revealed that areas prone to flooding are often low-lying and flat, or have shallow north- or northwest-facing slopes, are in close proximity to the stream network, and are situated on underlying impermeable lithology. The multi-criteria hybrid approach developed could classify 86.8 % of flood events correctly and produced a satisfactory validation result based on the receiver operating characteristic curve. The statistical method can be easily repeated and refined upon the availability of additional or higher quality data such as a high resolution digital elevation model. Additionally, the approach used in this study can be adopted to evaluate flood hazard in countries with similar characteristics, landscapes and climatic conditions, such as other Caribbean or Pacific Small Island Developing States.

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

Similar content being viewed by others

References

  • Abdul-Wahab S, Bakheit C, Al-Alawi S (2005) Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ Model Softw 20(10):1263–1271

    Article  Google Scholar 

  • Altman D (1991) Categorizing continuous covariates (letter to the editor). Br J Cancer 64:975

    Article  Google Scholar 

  • Altman D, Lausen B, Sauerbrei W, Schumacher M (1994) Dangers of using ‘optimal’ cutpoints in the evaluation of prognostic factors. J Nat Cancer Inst 86:829–835

    Article  Google Scholar 

  • Bates PD, Wilson MD, Horritt MS, Mason D, Holden N, Currie C (2006) Reach scale floodplain inundation dynamics observed using airborne Synthetic Aperture Radar imagery: data analysis and modelling. J Hydrol 328:306–318

    Article  Google Scholar 

  • Bathrellos GD, Kalivas DP, Skilodimou HD (2009) GIS-based landslide susceptibility mapping models applied to natural and urban planning in Trikala Central Greece. Estud Geol Madr 65(1):49–65

    Article  Google Scholar 

  • Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development by using natural hazard maps and geological—geomorphological parameters. Environ Earth Sci 66(2):537–548

    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 Environ Res Risk Assess 27(2):573–588

    Article  Google Scholar 

  • Beven K, Kirby M (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci B 24:43–69

    Google Scholar 

  • Burgess C, Taylor M, Stephenson T, Mandal A (2013) Extreme precipitation for Jamaica: 1895–2100. International Conference on Flood Resilience: experiences in Asia and Europe. Exeter, United Kingdom: University of Exeter

  • Burgess C, Taylor M, Stephenson T, Mandal A, Powell L (2015) A macro scale flood risk for Jamaica with impact of climate variability. Nat Haz 48(1):231–256

    Article  Google Scholar 

  • Carby B (2011) Mid-term review of the hyogo framework for action in the caribbean. In: Hutchinson G, Harris D (eds) University of the west indies, in PIOJ, 2012a. A Growth-Inducement Strategy for Jamaica. Planning Institute of Jamaica. Kingston, Jamaica

  • Chang H, Franczyk J, Kim C (2009) What is responsible for increasing flood risks? The case of Gangwon Province, Korea. Nat Haz 48(3):339–354

    Article  Google Scholar 

  • Chubey M, Hathout S (2004) Integration of RADARSAT and GIS modeling for estimating Red River flood risk. GeoJour 59:237–246

    Article  Google Scholar 

  • Chung C, Fabbri A (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Haz 30:451–472

    Article  Google Scholar 

  • Collymore J (2007) Disaster management in the Caribbean: perspectives on institutional capacity reform and development. Environ Haz 10(1):6–22

    Article  Google Scholar 

  • Collymore J (2011) Disaster management in the Caribbean: perspectives on institutional capacity reform and development. Environ Haz 10(1):6–22

    Article  Google Scholar 

  • CRIES (Comprehensive Resources Inventory and Evaluation Systems) (1985) Jamaica Resource Assessment, Comprehensive Resource Inventory and Evaluation System Project, Michigan State University, U.S. Department of Agriculture/Soil Conservation Service, Ohio State University, U.S.A. p 230

  • Dai F, Lee C, Li J, Xu Z (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391

    Article  Google Scholar 

  • Dixon B (2005) Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrol 309:37–38

    Article  Google Scholar 

  • ECLAC (2001) Jamaica: assessment of the damage caused by flood rains and landslides in association with hurricane Michelle, October 2001. Implications for economic, social and environmental development. p 46

  • Fekete A (2009) Validation of a social vulnerability index in context to river-floods in Germany. Nat Haz Earth Syst Sci 9:393–403

    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 

  • Grabs T, Seibert J, Bishop K, Laudon H (2009) Modeling spatial patterns of saturated areas: a comparison of the topographic wetness index and a dynamic distributed model. J Hydro 373:15–23

    Article  Google Scholar 

  • Guha-Sapir D, Below R, Hoyois PH (2010) EM-DAT: international disaster database. Université Catholique de Louvain, Brussels

    Google Scholar 

  • Hilsenbeck S, Clark G (1996) Practical p-value adjustment for optimally selected cutpoints. Stat Med 15:103–112

    Article  Google Scholar 

  • Hosmer D, Lemeshow S (2000) Applied logistic regression. Wiley, New York, p 383

    Book  Google Scholar 

  • Hsu K, Gupta HV, Gao X, Sorooshian S, Imam B (2002) Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis. Water Res Res 38(1):17–38

    Google Scholar 

  • Hu T, Lam K, Ng S (2001) River flow time series prediction with a range dependent neural network. Hydro Sci J 46(5):729–745

    Article  Google Scholar 

  • Hunter NM, Bates PD, Horritt MS, Wilson MD (2007) Simple spatially-distributed models for predicting flood inundation: a review. Geomorph 90(3–4):208–225

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jourde H, Lafare A, Mazzilli N, Belaud G, Neppel L, Dörfliger N, Cernesson F (2014) Flash flood mitigation as a positive consequence of anthropogenic forcing on the groundwater resource in a karst catchment. Environ Earth Sci 71(2):573–583

    Article  Google Scholar 

  • Keating A, Campbell K, Mechler R, Michel‐Kerjan E, Mochizuki J, Kunreuther H, Bayer J, Hanger S, McCallum I, See L, Williges K, Atreya A, Botzen W, Collier B, Czajkowski J, Hochrainer S, Egan C (2014) Operationalizing resilience against natural disaster risk: opportunities, barriers and a way forward, Zurich Flood Resilience Alliance

  • Kendall M (1957) A course in multivariate analysis. Griffin, London, p 185

    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 

  • Kim G, Barros A (2001) Quantitative flood forecasting using multisensor data and neural networks. J Hydrol 246(1–4):45–62

    Article  Google Scholar 

  • Kleinbaum DG, Klein M (2002) Modeling strategy for assessing interaction and confounding. In: Kleinbaum DG, Klein M (eds) Logistic regression: a self-learning text. Springer, New York

    Google Scholar 

  • Knebl M, Yang Z, Hutchison K, Maidment D (2005) Regional scale flood modeling using NEXRAD rainfall, GIS and HEC-HMS/RAS: a case study for the San Antonio river basin summer 2002 storm event. J Environ Manage 75:325–336

    Article  Google Scholar 

  • Lam K, Tau T, Lam MCK (2010) A Material supplier selection model for property developers using fuzzy principal component analysis. Auto Const 19:608–618

    Article  Google Scholar 

  • Langhammer J (2010) Analysis of the relationship between the stream regulations and the geomorphologic effects of floods. Nat Haz 54(1):121–139

    Article  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 Rem Sens 26(7):477–1491

    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, Song KY, Kim Y, Park I (2012) Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol J 20(8):1511–1527

    Article  Google Scholar 

  • Liu Y, De Smedt F (2005) Flood modeling for complex terrain using GIS and remote sensed information. Water Res Manage 19(5):605–624

    Article  Google Scholar 

  • Lulseged A, Hiromitsu Y, Norimitsu U (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

    Article  Google Scholar 

  • Mahyat S, Tehrany, Moung-Jin L, Pradhan B, Mustafa NJ, Lee S (2014) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72(10):4001–4015

    Article  Google Scholar 

  • Mandal A, Maharaj A (2013) Flooding in Jamaica with assessment of riverine inundation of Port Maria, St Mary. Bull de la Soc Geol de Fr 184(1–2):165–170

    Article  Google Scholar 

  • Merwade V, Cook A, Coonrod J (2008) GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping. J Environ Mod Soft 23:1300–1311

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

    Article  Google Scholar 

  • Nefeslioglu H, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171

    Article  Google Scholar 

  • Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Ghafari Gousheh M (2011) Assessment of input variables determination on the SVM model performance using PCA, Gamma test and forward selection techniques for monthly stream flow prediction. J Hydrol 401:177–189

    Article  Google Scholar 

  • Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou HD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping using seismic hazard in a rapid urban growing area. Eng Geol 158:77–88

    Article  Google Scholar 

  • PIOJ (Planning Institute of Jamaica) (2002) Jamaica: Macro-socio-economic Assessment of the Damage done by Flood Rains and Landslides May 2002. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/May%202002%20Flood%20Rains.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2004) Jamaica Macro-socio-economic and Environmental Assessment of the Damage done by Hurricane Ivan Sept 10–12, 2004. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/Hurricane%20Ivan.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2005a) Assessment of the Socio-economic and Environmental Impact of Hurricanes Dennis and Emily on Jamaica. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/Dennis%20Emily%20Report.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2005b) Assessment of the Socio-economic and Environmental Impact of Hurricane Wilma on Jamaica. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/Hurricane%20Wilma.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2007) Assessment of The Socio-economic and Environmental Impact of Hurricane Dean on Jamaica. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/Hurricane%20Dean.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2008) Assessment of the Socio-economic and Environmental Impact of Tropical Storm Gustav on Jamaica. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/Tropical%20Storm%20Gustav.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2009) Vision 2030 Jamaica—National Development Plan. Kingston, Jamaica: PIOJ. http://www.vision2030.gov.jm/. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2010) Jamaica Macro Socio-economic and Environmental Assessment of the Damage and Loss Caused by Tropical Depression No. 16/Tropical Storm Nicole. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/Portals/0/Sustainable_Development/Tropical%20Storm%20Nicole_Impact%20Assessment_Final.pdf. Accessed 8 Feb 2015

  • PIOJ (Planning Institute of Jamaica) (2012) Jamaica Macro Socio-economic and Environmental Assessment of the Damage and Loss caused by Hurricane Sandy. Kingston, Jamaica: PIOJ. http://pioj.gov.jm/ResearchandData/tabid/82/Default.aspx. Accessed 8 Feb 2015

  • Porporato A, Ridolfi L (1997) Nonlinear analysis of river flow time sequences. Water Res Res 33:1353–1367

    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 

  • Quinn P, Beven K, Lamb R (1995) The ln(a/tanβ) index : how to calculate it a n d how to use it within the topmodel framework. Hydrol Proc 9:161–182

    Article  Google Scholar 

  • Rajurkar M, Kothyari U, Chaube U (2004) Modeling of the daily rainfall runoff relationship with artificial neural network. J Hydrol 285:96–113

    Article  Google Scholar 

  • Remondo J, González A, Díaz de Terán JR (2003) Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain. Nat Hazards 30:437–449

    Article  Google Scholar 

  • Roohollah N, Khakpour A, Omidvar B, Farokhnia A (2010) Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Sys Appl 37:5856–5862

    Article  Google Scholar 

  • Rozos D, Bathrellos GD, Skilodimou HD (2011) Comparison of the implementation of rock engineering system (RES) and analytic hierarchy process (AHP) methods, based on landslide susceptibility maps, compiled in GIS environment. A case study from the Eastern Achaia County of Peloponnesus, Greece. Environ. Earth Sci 63(1):49–63

    Article  Google Scholar 

  • Saleh S, Al-Hatrushi S (2009) Torrential Flood Hazards Assessment, Management, and Mitigation, in Wadi Uday, Muscat Area, Sultanate of Oman, a GIS & RS approach Egypt. J Rem Sens Space Sci 12:71–86

    Google Scholar 

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

    Article  Google Scholar 

  • Schoof J, Pryor S (2001) Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. Int J Clim 21:773–790

    Article  Google Scholar 

  • Sivakumar B (2001) Rainfall dynamics at different temporal scales: a chaotic perspective. Hydrol Earth Sys Sci 5(4):645–651

    Article  Google Scholar 

  • Sivakumar B, Berndtsson R, Olsson J, Jinno K (2001) Evidence of chaos in the rainfall-runoff process. Hydrol Sci 46(1):131–145

    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(2):197–204

    Article  Google Scholar 

  • Sørensen R, Seibert J (2007) Effects of DEM resolution on the calculation of topographical indices: TWI and its components. J Hydrol 347(1–2):79–89

    Article  Google Scholar 

  • Sousa S, Martins F, Alvim-Ferraz M, Pereira M (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Mod Softw 22:97–103

    Article  Google Scholar 

  • Taylor MA, Mandal A, Burgess C, Stephenson T (2014) Flooding and climate change: sectorial impacts and adaptation strategies for the caribbean region, Chap 10. In: Dave D, Chadee, Joan M, Sutherland, John B, Agard (eds) Flooding in Jamaica: causes and controls. Nova Science Pub Inc, New York, pp 163–187

  • 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 

  • Townsend PA, Walsh S (1998) Modeling floodplain inundation using an integrated GIS with Radar and optical remote sensing. Geomorph 21:295–312

    Article  Google Scholar 

  • Tsai F, Li X (2008) Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window. Water Res Res 44:W09434

    Article  Google Scholar 

  • UNISDR (United Nations Office of Disaster Risk Reduction) (2011) Global assessment report on disaster risk reduction. United Nations International Strategy for Disaster Reduction, Geneva

    Google Scholar 

  • Verstraeten G, Poesen J (2001) Modelling the long term sediment trap efficiency of small ponds. Hydrol Proc 15:2797–2819

    Article  Google Scholar 

  • Wang S, Xiao F (2004) AHU sensor fault diagnosis using principal component analysis method. Energy Build 36(2):147–160

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266

    Article  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 

  • Zhu D, Ren Q, Xuan Y, Chen Y, Cluckie ID (2013) An effective depression filling algorithm for DEM-based 2-D surfaceflow modelling Guangzhou, China. Hydrol Earth Syst Sci 17:495–505

    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 13:67–576

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the Water Resources Authority, Meteorological Service of Jamaica and the Office of Disaster Preparedness and Emergency Management, Jamaica for providing the spatial datasets and historical flood data. The authors also acknowledge the Climate Development Knowledge Network (CDKN) and Caribsave for funding the research project of which the present study is a component.

Author information

Authors and Affiliations

  1. Department of Geosciences, East Tennessee State University, Tennessee, USA

    Arpita Nandi

  2. Department of Geography and Geology, University of the West Indies, Mona, Jamaica

    Arpita Mandal

  3. Department of Geography, University of the West Indies, St. Augustine, Trinidad and Tobago

    Matthew Wilson

  4. Disaster Risk Reduction Center, Institute of Sustainable Development, University of the West Indies, Mona, Jamaica

    David Smith

Authors
  1. Arpita Nandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arpita Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arpita Nandi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nandi, A., Mandal, A., Wilson, M. et al. Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ Earth Sci 75, 465 (2016). https://doi.org/10.1007/s12665-016-5323-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-016-5323-0

Keywords

Search

Navigation