Flood susceptibility zonation using new ensemble Bayesian-AHP methods (Case study: Neka Watershed, Mazandaran Province)

Document Type : Research Article

Authors

1 PhD Candidate of Geomorphology, Tarbiat Modares University, Tehran, Iran

2 Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

3 Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran

Abstract

Flood susceptibility mapping is the first step in flood management programs. Flood prediction can help reduce its following damages. The main objective of this study is identification of prone areas to flooding using new ensemble Bayesian-AHP methods in the Neka-Sari watershed, Iran. Flood inventory map was prepared based on statistical analyses. A total of 240 (70 %) and 102 (30 %) out of 342 observed events were used as training and validation data set, respectively. Based on literature review and extensive field studies, a total of 11 parameters in relation to flood occurrences were selected for flood mapping, including slope percent, elevation, distance to river, drainage density, NDVI, lithology, land use, topography wetness index (TWI), stream power index (SPI), rainfall, and curvature. The weights of each factor were determined by AHP method. Also, the relation between factor classes and flood events and the weight of each class were estimated using Bayesian theory. Finally, by integration of factors and their classes in ArcGIS, flood susceptibility map was obtained with five classes. In order to evaluate the obtained model, ROC curve was employed. Results showed that the ensemble model had a high accuracy (76.10 %) in flood susceptibility mapping. Also, slope percent, elevation, and land use had the highest effect on flood events with values of 0.260, 0.195, and 0.146, respectively. According to the results, 24.17 and 37.15 % of the study area are categorized in high and very high susceptibility classes, respectively. The presented combined model can be used for further studies on natural hazard mapping and disaster management.

Keywords

Main Subjects

References

 
[1]. Du J, Fang J, Xu W, Shi P. Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province China. Stoch Env Res Risk Assess. 2013; 27(2): 377–387.
[2]. Jahangir MH, Sadeghi S, Soleymani H. Numerical Evaluation of Maximum Flood Discharge Using SCS Method for Land Management on Watersheds of Kan Area. Ecohydrology. 2014; 1: 47-57. [Persian]
[3]. Yang YCE, Ray PA, Brown CM, Khalil AF, Yu WH. Estimation of flood damage functions for river basin planning: a case study in Bangladesh. Nat Hazards. 2015; 75: 2773-2791.
[4]. Hudson P, Botzen WJW, Kreibich H, Bubeck P, Aerts JCJH. Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching. Nat Hazards Earth Syst Sci. 2014; 14:1731-1747.
[5]. Perera EDP, Hiroe A, Shrestha D, Fukami K, Basnyat DB, Gautam S, et al. Community-based flood damage assessment approach for lower West Rapti River basin in Nepal under the impact of climate change. Nat Hazards. 2015; 75: 669-699.
[6]. Foudi S, Os_es-Eraso N, Tamayo I. Integrated spatial flood risk assessment: the case of Zaragoza. Land Use Policy. 2015; 42: 278-292.
[7]. Tehrany MS, Lee MJ, Pradhan B, Jebur MN, Lee S. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci. 2014; 71 (10): 4001-4015.
[8]. Yousefi H, Noorollahi Y, Soltani K, Javadzadeh, Z. The Management Strategies to Reduce the Vulnerability of Flood in Tehran (Case Study: District 1 and 3). Ecohydrology. 2015; 3: 181-193. [Persian]
[9]. Esmaili F, Rahmani S. Flood Zoning Using GIS and Mathematical Models Emphasizing Flood Management: A Case Study of Gavi River, Ilam Province, Western Iran. International Bulletin of Water Resources & Development. 2015; 5 (8): 63-73. [Persian]
[10]. Heidari A. Flood vulnerability of the Karun river system and short-term mitigation measures. Flood Risk Manag. 2014; 7: 65-80.
[11]. Cook A, Merwad V. Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping,. Journal of Hydrology. 2009; 377: 131–142.
[12]. Khalilizadeh M, Mosaedi A, Najafinejad A. Flood hazard zonation in a part of Ziarat river in Gorgan urban watershed. J.Agric.Sci. Natur. Resour. 2005; 12 (4): 138-146. [Persian]
[13]. Dehghani M, Abbasnejad A, Negaresh H. Assessment of Flood Hazard and its Zoning in Baft Plain (South East Part of Iran), Geography and Territorial Spatial Arrangement. 2016; 6: 141-152. [Persian]
[14]. Poussin JK, Botzen WJW, Aerts JCJH. Factors of influence on flood damage mitigation behavior by households. Environ Sci Policy. 2014; 40: 69-77.
[15]. Farzin S, Karami H, Doostmohammadi M, Ghanbari A, Zamiri, E. The performance of Artificial Neural Network in prediction and analysis of hydrological processes (Case study: Water shortage in Nazloo-chai watershed, West Azerbaijan province). Ecohydrology. 2017; 4: 631-644. [Persian]
[16]. Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol. 2014; 11: 909-926.
[17]. Regmi N, Giardino JR, Vitek JD. Modeling susceptibility to landslides using the ‎weight of evidence approach: Western Colorado,USA. Geomorphology. 2010; 115: 172-187.
[18]. Pourghasemi HR, Moradi HR, Mohammdi M, Mostafazadeh R, Goli Jirandeh A. Landslide Hazard Zoning Using Bayesian Theory. JWSS - Isfahan University of Technology. 2013b; 16 (62):109-120. [Persian]
[19]. Mohamadi E, Montaseri M, Sokooti Oskoei R. Zonation of flood dangers in urban regions, using WMS and HEC-RAS, case study: Oshnavieh, Western Azerbyjan province. Watershed Engineering and Management. 2005; 1 (1): 61-69.
[20]. Ebrahimi P, soleymani K, shahedi, K. Developing Strategic Environmental Planning map Based on Land use Changes and Flood Zones Case study: Neka River. Geography and Territorial Spatial Arrangement. 2016; 20: 57-74. [Persian]
[21]. Chen YR, Yeh CH, Yu B. Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat Hazards. 2011; 59(3): 1261–1276.
[22]. Rahmati O, Zeinivand H, Besharat M. Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics. Natural Hazards and Risk. 2015; 7(3): 1000-1017.
[23]. Tehrany MS, Pradhan B, Mansor S, Ahmad N. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena. 2015; 125: 91–101.
[24]. Tehrany MS, Pradhan B, Jebur MN. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol. 2014; 512: 332–343.
[25]. Youssef AM, Pradhan B, Sefry SA. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci. 2016; 75:12.
[26]. Zehra S, Afsar S. Flood Hazard Mapping of Lower Indus Basin Using Multi-Criteria Analysis. Journal of Geoscience and Environment Protection. 2016; 4: 54- 62.
[27]. Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR. 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. 2016; DOI 10.1007/s11069-016-2357-2.
[28]. Fernandez DS, Lutz MA. Urban flood hazard zoning in Tucum_an Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol. 2010; 111: 90-98.
[29]. Pradhan B. Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci. 2009; 1(1): 120 –129.
[30]. Yesilnacar E, Topal T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol. 2005; 79: 251–266.
[31]. Jebur MN, Pradhan B, Tehrany MS. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ. 2014b; 152: 150–165.
[32]. Pike RJ. Geomorphology - Diversity in quantitative surface analysis. Progress in Physical Geography. 2000; 24:1-20.
[33]. Pradhan B, Lee S. 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. 2010; 25(6): 747–759.
[34]. Pourghasemi HR, Pradhan B, Gokceoglu C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed. Iran. Nat Hazards. 2012; 63(2): 965–996.
[35]. Pradhan B. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci. 2013; 51: 350–365.
[36]. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR, Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab. J. Geosci. 2013a; 6 (7): 2351–2365.
[37]. Jebur MN, Pradhan B, Tehrany MS. Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia, using L-band InSAR technique. Geosci J. 2014a; 18(1): 61–68.
[38]. Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 2013c; 2: 349–369.
[39]. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian J Geosci. 2013d; 6: 2351–2365.
[40]. Pourghasemi HR, Kerle N. Random forest-evidential belief function based landslide susceptibility assessment in western Mazandaran Province, Iran. Environ. Earth Sci. 2016; 75:185.
[41]. Piacentinia D, Troiani F, Soldati M, Notarnicola C, Savelli D, Schneiderbauer S, et al. Statistical analysis for assessing shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy). Geomorphology. 2012; 151: 196–206.
[42]. Arabameri AR, Halabian AH. Landslide Hazard Zonation Using Statistical Model of AHP ‎‎(Case ‎Study: Zarand Saveh Basin). Physical Geomorphology. 2015; 28: 65-86. [Persian]
[43]. Ayalew L, Yamagishi H, Marui H, Kanno T, Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng. Geology. 2005; 81: 432–445.
[44].Saaty TL. The Analytical Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw Hill. 1980; 287 p.
[45]. Nefeslioglu H, Gokceoglu C, Sonmez H. 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. 2008; 97(3): 171–191.
[46]. Swets JA, Measuring the accuracy of diagnostic systems. Science. 1988; 240: 1285–1293.
[47]. Fernandez DS, Lutz MA. Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol. 2010; 111: 90-98.
[48]. Arabameri AR, Klorajan A, Karami J, Alimoradi M, Shirani K. Zonation of Landslide Hazard Using Artificial Neural Network the Case Study: Marbor Basin. Geodynamics Research International Bulletin. 2014; 03: 44-59. [Persian]
 [49]. Arabameri AR, Shirani K. identification of effective factors on Landslide occurrence and its ‎hazard zonation using Dempster-Shafer theory (Case study: Vanak Basin, Isfahan Province). Watershed Engineering and Management. 2016; 8 (1): 93-106. [Persian] ‎
[50]. Arabameri AR, Shirani K, Karami J, Kolorajan A. Application of neural network of Multi Layers Perceptron (MLP) in site selection of waste disposal (Case ‎study: Fereydoonshahr city). Environmental Studies. 2016; 42 (2): 329-341. [Persian]
 [51]. Gokceoglu C, Sonmez H, Nefeslioglu HA, Duman TY, Can T. The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol, 2005; 81(1): 65–83.
Iranian journal of Ecohydrology
Volume 4, Issue 2
June 2017
Pages 447-462

Files

History

  • Receive Date: 27 November 2016
  • Revise Date: 21 February 2017
  • Accept Date: 21 February 2017
  • First Publish Date: 22 June 2017
  • Publish Date: 22 June 2017

Share

How to cite

Statistics