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

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Iranian journal of Ecohydrology
Volume 8, Issue 1
April 2021
Pages 89-108

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History

  • Receive Date: 24 June 2020
  • Revise Date: 18 February 2021
  • Accept Date: 18 February 2021
  • First Publish Date: 08 March 2021
  • Publish Date: 21 March 2021

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