Abstract
This paper investigates the application of logistic regression model for flood susceptibility mapping in southern Gaza Strip areas. At first, flood inventory maps were identified using Palestinian Water Authorities data and extensive field surveys. A total of 140 flood locations were identified, of which 70% were randomly used for data training and the remaining 30% were used for data validation. In this investigation, six causing flood variables from the spatial database were prepared, which are digital elevation model (DEM), topographic slope, flow accumulation, rainfall, land use/land cover (LULC), and soil type. Then, comprehensive statistical analysis techniques including Pearson’s correlation, multicollinearity, and heteroscedasticity analyses were used, to ensure that the regression assumptions are not violated. The uniqueness of the current study is its inclusiveness of influential causing flood parameters and vigorous statistical analyses that led to accurate flood prediction. Quantitatively, the proposed model is robust with very reasonable accuracy. The prediction and success rates are 76 and 81%, respectively. The practical and unique contribution of this investigation is the generation of flood susceptibility map for the region. This is a very useful tool for the decision makers in the Gaza Strip to reduce human harm and infrastructure losses.
Similar content being viewed by others
References
Adiat K, Nawawi M, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool—a case of predicting potential zones of sustainable groundwater resources. J Hydrol 440–441:75–89
Aish A, De Smedt F (2004) Modeling of a groundwater mound resulting from artificial recharge in the Gaza Strip, Palestine, Proceedings of the Second Israeli-Palestinian International Conference on Water for Life in the Middle East, October 10–14, 2004, Turkey, pp 114–122
Akay H, Baduna Kocyigit M, Yanmaz AM (2018) Effect of using multiple stream gauging stations on hydrologic parameters and estimation of hydrograph of ungauged neighboring basin. Arab J Geosci 11:282
Al-Agha M (1995) Environmental contamination of groundwater in the Gaza Strip. Environ Geol 25:109–113
Al-Juaidi AE (2018) A simplified GIS based SCS-CN method for the assessment of land use change on runoff. Arab J Geosci 11:269
Al-Juaidi AE, Kaluarachchi JJ, Kim U (2010) Multi-Criteria Decision Analysis of Treated Wastewater Use for Agriculture in Water Deficit Regions. J Am Water Resour Assoc 46(2):395–411
Al-Juaidi AE, Kaluarachchi JJ, Mousa AI (2014) Hydrologic-economic model for sustainable water resources management in a coastal aquifer. J Hydrol Eng ASCE 19(11):04014020
Al-Juaidi AE, Rosenberg DE, Kalaruchchi JJ (2009) Water Management with wastewater treatment and reuse, desalination, and conveyance, to counteract climate change in the Gaza Strip. AWRA Specialty Conference on Climate Change, Anchorage, Alaska, 4–6 May 2009
Al-Juaidi AE, Rosenberg DE, Kaluarachchi JJ (2011) Water management with wastewater treatment and reuse, desalination, and conveyance to counteract future water shortages in the Gaza Strip. Int J Water Resour Environ Eng 3(12):266–282
Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385
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
Baduna Kocyigit M, Akay H, Yanmaz AM (2017) Effect of watershed partitioning on hydrologic parameters and estimation of hydrograph of an ungauged basin: a case study in Gokirmak and Kocanaz, Turkey. Arab J Geosci 10:331
Chaney NW, Herman JD, Reed PM, Wood EF (2015) Flood and drought hydrologic monitoring: the role of model parameter uncertainty. Hydrol Earth Syst Sci 19:3239–3251
Chau KT, Chan JE (2005) Regional bias of landslide data in generating susceptibility maps; case of Hong Kong Island. Landslides 2:280–290
Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7(4):411–423
Chormanski J, Okruszko T, Ignar S, Batelaan O, Rebel KT, Wassen MJ (2011) Flood mapping with remote sensing and hydrochemistry: a new method to distinguish the origin of flood water during floods. Ecol Eng 37:1334–1349
Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472
Dai FC, Lee FC, Tham LG, Ng KC, Shum WL (2004) Logistic regression modelling of storm-induced shallow landsliding in time and space on natural terrain of Lantau Island, Hong Kong. Bull Eng Geol Environ 63:315–327
Eicker F (1967) Limit theorems for regression with unequal and dependent errors. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp 5982
Fernando N, S Grimaldi S, Santini M, Petroselli A, Ubertini L (2008) Hydrogeomorphic properties of simulated drainage patterns using digital elevation models: the flat area issue. Hydrol Sci J 53(6):1176–1193
Fisher FM et al (2005) Liquid assets: an economic approach for water management and conflict resolution in the middle east and beyond, resources for the future. Routledge, Washington, DC, p 272
Gujarati DN (2004) Basic econometrics, 4th edn. The MacGraw Hill Company, New York City 1002 p
Helsel DR, Hirsch RM (2002) Statistical methods in water resources—hydrologic analysis and interpretation: U.S. Geological Survey Techniques of Water-Resources Investigations, book 4, chap A3, 510 p
Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley series in probability and mathematical statistics. Wiley, New York, p 307
Hsu T, Shih D, Chen W (2014) Destructive flooding induced by broken embankments along Linbian Creek, Taiwan, during typhoon Morakot. J Hydrol Eng ASCE 20(7):05014025,1–05014025,9
Huber PJ (1967) The behavior of maximum likelihood estimates under nonstandard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp 221–233
Iwalewa TM, Elamin AS, Kaka SI (2016) A coupled model simulation assessment of shallow water-table rise in a Saudi Arabian coastal city. J Hydro Environ Res 12:46–58
Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WN, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251–264
Kleiber C, Zeileis A (2006) Applied Econometrics with R. User-2006 conference. Archived from the original (PDF) on April 22, 2007
Konadu D, Fosu C (2009) Digital elevation models and GIS for watershed modelling and flood prediction—a case study of Acca Ghana. In: Appropriate Technologies for Environmental Protection in the developing world. Springer, pp 352–332
Kourgialas NN, Karatzas GP (2011) Flood management and GIS modelling method to assess flood-hazard areas—a case study. Hydol Sci J 56(2):212–225
Kutner MH, Nachtsheim CJ, Neter J (2004) Applied linear regression models, 4th edn. McGraw-Hill Irwin
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41
Ohlmacher G, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansa, USA. Eng Geol 69:331–343
Omid 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(1):42–70
Palestinian Water Authority (PWA) (2012) Status report of water resources in the occupied state of Palestine, year of flooding water harvesting, Annual water resources status report, 17 p
Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou KD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Eng Geol 158:77–88
PEPA (1996) Gaza environmental profile, Part one-inventory of resources. Palestinian Environmental Protection Authority (PEPA), Gaza, pp 1–21
Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J 2(3):643–662
Pradhan B (2010) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9:1–18
Pradhan B, Lee S (2009) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci, on-line first 60:1037–1054
Sarhadi A, Soltan S, Modarres R (2012) Probabalistic flood inundation mapping of ungauged rivers: linking GIS techniques and frequency analysis. J Hydrol 458:68–86
Schumann GJ, Bates PD, Neal JC, Andreadis KM (2014) Technology: fight floods on a global scale. Nature 507:169
Shirzadi A, Saro L, Joo OH, Chapi K (2012) A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Nat Hazards 64:1639–1656
Tehrany MS, Lee MJ, Pradhan B, Jebur M, Lee S (2014a) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72:4001–4015
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
Tehrany MS, Pradhan B, Mansor SH, Ahmed N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101
Wagener T, Sivapalan M, Troch P, Woods R (2007) Catchment classification and hydrologic similarity. Geogr Compass 1(4):901–931
White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4):817–838
Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey [PhD thesis]. Melbourne: Department of Geomatics the University of Melbourne, 423 p
Youssef AM, Pradhan B, Sefry SA (2016) Flash flood susceptibility assessment in Jeddah city (Kindom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci 75(12)
Acknowledgments
The authors would like to thank the anonymous reviewers for their valuable comments on the manuscript.
Author information
Authors and Affiliations
Contributions
Ahmed E. M. Al-Juaidi conducted the validation test and prepared the manuscript. Ayman M. Nassar analyzed the GIS maps. Omar E. M. Al-Juaidi performed the statistical analysis.
Corresponding author
Rights and permissions
About this article
Cite this article
Al-Juaidi, A.E.M., Nassar, A.M. & Al-Juaidi, O.E.M. Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab J Geosci 11, 765 (2018). https://doi.org/10.1007/s12517-018-4095-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-018-4095-0