Evaluation and zonation of Landslide hazard with using OWA and ANN methods (case study: Paveh Township)

Document Type : Research Article

Authors

1 Associate professor of Geomorghology, University of Mohaghegh Ardabili, Iran

2 MS.c in RS and GIS, University of Mohaghegh Ardabili, Iran

3 PhD Student of Geomorghology, University of Mohaghegh, Iran

Abstract

Paveh Township has long been affected by landslides due to specific geological and geomorphologic features and anthropic activities. This study aimed to map landslide risk and its relationship with factors affecting their occurrence and compare the ANN model with (OWA) method to assess landslide risk in Paveh Township. Therefore, landslides in the area were first identified using extensive field surveys. Maps of factors affecting landslide occurrence (lithology, slope, slope direction, elevation, precipitation, land use, distance from the waterway, distance from the road, distance from the fault, soil) in GIS software then extract the relevant layers Was done. To perform the OWA model, weighting was performed by the fuzzy method using the Critical and Evaluation and Standardization of benchmark maps and to perform an artificial neural network (MATLAB) software. Each neural network parameter was determined by trial and error method. Then with the final structure of the network with 8 neurons in the input layer, 13 neurons in the hidden layer, and 1 neuron in the output layer. According to the results of the study of slope factors, land use, lithology, and soil, respectively, by weight factor; 0.156, 0.143, 0.139, and 0.131, received the most importance. Which according to the model output (OWA) was 15.53 and 26.64%, respectively, in two very high and high-risk classes, respectively. Due to the output of the neural network 19.88% and 29.82% of the area is located on the high-risk floor. Very high-risk and high-risk areas are mainly located in 15-30% slope, agricultural use, unbearable and weak quaternary structures, and in soils with a high percentage of clay, silt, and marl. The two models were compared and the OWA model had higher accuracy.

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References

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History

  • Receive Date: 24 March 2020
  • Revise Date: 11 February 2021
  • Accept Date: 11 March 2021
  • First Publish Date: 14 April 2021
  • Publish Date: 23 August 2021

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