Efficiency Evaluation of the VIKOR, L-THIA, and Artificial Neural Network (ANT) Models in Flood Zone Analysis (Case Study: Khorasan Razavi Province)

Document Type : Research Article

Authors

1 Associate Professor of Geomorphology, Hakim Sabzevari University, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Iran

2 Professor of Geomorphology, Hakim Sabzevari University, Faculty of Geography and Environmental Sciences, Iran

3 Graduated from Geomorphology, Hakim Sabzevari University, Faculty of Geography and Environmental Sciences, Iran

Abstract

Considering the natural conditions of Iran, not paying attention to floods can cause irreparable damages, among which flood estimation and zoning of floodplain areas are very significant in controlling hazards, so zoning of climate change is necessary. The present study aims to investigate the risk of floods in selected basins of Khorasan Razavi using the VIKOR, L-THIA, and ANT models. Then, fourteen variables affecting the occurrence of floods including climate, land use, altitude, drainage density, geomorphological units, lithology, run-off height, permeability, slope and direction, distance to rivers/waterways, precipitation, temperature, and soil were used. The results showed that among the mentioned variables, climate parameters, land use, slope, drainage density, distance to rivers/waterways, precipitation, soil, and geomorphological units have greater effects on the occurrence of floods according to statistical calculations. Quantitative and qualitative evaluation of the results using various statistics showed that the L-THIA model, with a γ=0.8, had the highest correlation with the primary layers and was more accurate and efficient than the two VIKOR and ANT models in flood prediction.

Keywords

References

[1]. Topaloulu F. Determining suitable probability distribution models for flow and precipitation series of the Seyhan river basin. Turkish Journal of Agriculture and Forestry. 2002; 26(5): 187-194.
 
[2]. Asghari Saraskanrood S, Piroozi E, Zeinali B. Flood risk zoning in Aghlaghan Chay watershed using Vickor model. Quantitative Geomorphological Research. 2015; 4(3): 231-245. [Persian]
[3]. Mehdizadeh J. Climate hazard analysis in Tabriz using fuzzy logic and ANP Model, Master’s thesis in Geography. University of Mohaghegh Ardabili, Ardabil. 2011: 1-163. [Persian].
[4]. Kia M, Soft Computing using MATALAB (5th Ed). Kian Publication. Iran. P.134.
 [5]. Chen Y, Zhou H, Zhang H, Du G, Zhou J. Urban flood risk warning under rapid urbanization, Environmental research. 2015; 139(4): 3-10.
[6]. Afrooz B. Presenting an appropriate model in leveling the performance of urban management in laying the groundwork for entrepreneurship development (case study: Ardabil City). Supervisor: Ata Ghaffari Gilandeh, Master’s thesis, Department of Geography and Urban and Rural Planning. University of Mohaqeq Ardabili. Ardabil. 2011: 1-153. [Persian].
[7]. Kain C.L, Rigby E.H, Mazengarb C. A combined morphometric, sedimentary, GIS and modelling analysis of flooding and debris flow hazard on a composite alluvial fan. Caveside, Tasmania. Sedimentary Geology, 2018; 64(11): 286-301.
[8]. Rezaei Moghadam M. H, Rajabi M, Danesh Faza R, Kheirizadeh M. Zoning and study of morphological effects of Zarrineh River floods (from Sari Qomish to Norouzlu Dam). Journal of Geography and Environmental Hazards. 2016; 17(3): 1-20. [Persian].
[9]. Rad M, Vafakhah M, Gholamalifard M. Flood mapping using HEC-RAS hydraulic model in part of Khorramabad watershed. Journal of Natural Environmental Hazards. 2018; 7(5): 211-226. [Persian].
[10]. Dawson C, Abrahart A. Y, Shamseldin R. L. Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology. 2016; 319(1-4): 391-409.
[11]. Esfandiari Darabad F, Rahimi M, Gholamreza Pour M. Flood Flood zonation of Agerloo Cay Basin using the L-THIA method and fuzzy logic. Quantitative Geomorphological Research. 2019; 8(3): 71-155. [Persian].
[12]. Abedini M, Fathi Jokdan R. Flood Risk Zoning in the Karganroud’s Catchment Basin Using ArcGIS. Hydrogeomorphology. 2016; 7(11): 1-17. [Persian].
[13]. Gaňová L, Zeleňáková M, Purcz P, Diaconu D. C, Orfánus T, Kuzevičová Ž. Identification of urban flood vulnerability in eastern Slovakia by mapping the potential natural sources of flooding-implications for territorial planning. Urbanism Architecture Constructions. 2017; 8(4): 365-376.
[14]. Lee G, Choi J, Jun K. S. MCDM approach for identifying urban flood vulnerability under social environment and climate change. Journal of Coastal Research. 2017; 79(1): 209-213.
[15]. Nadiri M. Flood Risk Zoning Using TOPSIS AHP Fuzzy Logic in GIS Environment (Case Study of Aydoghmush Watershed). Geography Quarterly (Regional Planning). 2019; 9(3): 293-306. [Persian].
[16]. Tehrany M, Pradhan S, Jebur B. Flood Susceptibility Mapping Using a Novel Ensemble Weights-of Evidence and Support Vector Machine Models in GIS. Journal of Hydrology, 2014; 512(33): 332-343.
[17]. Kurtulus B, Razack, M. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. Journal of Hydrology , 2010; 381(1): 101-111.
[18]. Karim M. A, Chowdhury J.U. A comparison of four distributions used in flood frequency analysis in Bangladesh. Hydrological Sciences Journal. 1995; 40(1): 55-66. [Persian].
[19]. Hassannia D, Azari Amghani R, Valizadeh K. Flood zoning and its impact on land use in the surrounding area using unmanned aerial vehicles (UAV) images and GIS. Journal of RS and GIS for Natural Resources. 2019; 10 (3): 59-74. [Persian].
[20]. Hejazi A, Khodaei Gheshlagh F, Khodaei Gheshlagh L. Zoning of flood risk in the Warkash Chay catchment using HEC-RAS software and HEC-GEO add-on. Journal of Applied Researches in Geographical Sciences. 2019; 1 (19): 33-53. [Persian].
[21]. Ashour H. Study and analysis of the appropriateness and attractions of Amol industrial town in the location of industrial units, Supervisor Ata Ghaffari Gilandeh, Master’s Thesis, Department of Geography and Urban and Rural Planning. University of Mohaghegh Ardabili, Ardabil. 2011: 1-170. [Persian].
[22]. Yang TH, Ho J, Hwang GD, Lin GF. An indirect approach for discharge estimation: a combination among micro-genetic algorithm. hydraulic model and in situ measurement. Flow Measurement and Instrumentation. 2014; 39(8): 46-53.
[23]. Sharifi Garmadreh E, Vafakhah M, Eslamia
S.S. Assessment the Performance of Support Vector Machine and Artificial Neural Network Systems for Regional Flood Frequency Analysis (A Case Study: Namak Lake Watershed). JWSS. 2019; 23(1): 351-366. [Persian].
Iranian journal of Ecohydrology
Volume 8, Issue 1
April 2021
Pages 89-108

Files

History

  • Receive Date: 24 June 2020
  • Revise Date: 18 February 2021
  • Accept Date: 18 February 2021

Share

How to cite

Statistics