Comparison of Artificial Neural Network Model with Analytical Hierarchy Process in Landslide Hazard Assessment Using Geographic Information Systems

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Belvasi Iman Ali; REZAEI MOGHADDAM MOHAMMAD HOSSEIN; NIKJOO MOHAMMAD REZA; VALIZADEH KAMRAN KHALIL

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Journal: ENVIRONMENTAL HAZARDS MANAGEMENT Year:2015 | Volume:2 | Issue:2 Page(s): 225-250

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Title

Comparison of Artificial Neural Network Model with Analytical Hierarchy Process in Landslide Hazard Assessment Using Geographic Information Systems

Author(s)

Belvasi Iman Ali | REZAEI MOGHADDAM MOHAMMAD HOSSEIN | NIKJOO MOHAMMAD REZA | VALIZADEH KAMRAN KHALIL | Issue Writer Certificate

Keywords

Alashtar Doab watershedQ2

artificial neural networkQ2

GIS and AHPQ2

landslideQ2

Abstract

landslide is one of the natural hazards in mountainous regions that results in huge losses every year. Alashtar Doab watershed with mountainous terrains, uplands and different natural conditions has the potential for landslide. The purpose of this study is to compare the ANN1 model with AHP to evaluate landslide in Alashtar Doab watershed. In order to prepare the map, first of all, parameters of the landslide were extracted and then the layers were prepared and after that a landslide distribution map that was occurred in the basin was prepared and then by combining landslide influencing factors with landslide distribution map, the impact of each of these factors such as slope, aspect, elevation, lithology, rainfall, land use, distance from fault and stream in ArcGIS software were measured. In this study, in order to zone landslide hazard in Alashtar Doab watershed, the ANN and AHP2 were used. Back propagation algorithm and sigmoid activation function were used in ANN. The final structure of the network consisted of eight neurons in the input layer, eleven neurons in the hidden layer and one neuron in the output layer. After optimization of the network structure, all area information was imported to the network and finally, landslide hazard zoning map was prepared according to output weight. In AHP method, after paired comparisons and extracting the weight of parameters, the potential landslide area was obtained by combining them. The kappa statistic factor was used for assessment and classification output results of model that were used to estimate landslide hazard. The result shows that the ANN model with 0. 9 kappa coefficient is a more efficient method than AHP to map landslide hazard in Alashtar Doab watershed.

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APA: Copy

Belvasi, Iman Ali, REZAEI MOGHADDAM, MOHAMMAD HOSSEIN, NIKJOO, MOHAMMAD REZA, & VALIZADEH KAMRAN, KHALIL. (2015). Comparison of Artificial Neural Network Model with Analytical Hierarchy Process in Landslide Hazard Assessment Using Geographic Information Systems. ENVIRONMENTAL HAZARDS MANAGEMENT, 2(2 ), 225-250. SID. https://sid.ir/paper/265008/en

Vancouver: Copy

Belvasi Iman Ali, REZAEI MOGHADDAM MOHAMMAD HOSSEIN, NIKJOO MOHAMMAD REZA, VALIZADEH KAMRAN KHALIL. Comparison of Artificial Neural Network Model with Analytical Hierarchy Process in Landslide Hazard Assessment Using Geographic Information Systems. ENVIRONMENTAL HAZARDS MANAGEMENT[Internet]. 2015;2(2 ):225-250. Available from: https://sid.ir/paper/265008/en

IEEE: Copy

Iman Ali Belvasi, MOHAMMAD HOSSEIN REZAEI MOGHADDAM, MOHAMMAD REZA NIKJOO, and KHALIL VALIZADEH KAMRAN, “Comparison of Artificial Neural Network Model with Analytical Hierarchy Process in Landslide Hazard Assessment Using Geographic Information Systems,” ENVIRONMENTAL HAZARDS MANAGEMENT, vol. 2, no. 2 , pp. 225–250, 2015, [Online]. Available: https://sid.ir/paper/265008/en

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