چکیده:
The purpose of this study was to investigate and compare the two models of artificial neural network and TOPSIS model in the risk of landslide in the downstream of Sanandaj dam. This was done using ARCGIS software, Python programming language, and artificial neural network models and TOPSIS. For this purpose, 9 input layers were used in landslide risk zoning. Landslide and non-slip points in the area were determined using satellite imagery. Internal weighting was used to determine the weight of the layers. In the neural network model, the data were trained using a multilayer perceptron network with the Adam learning algorithm. The network structure has 9 neurons in the input layer, 30 neurons in the middle layer and 1 neuron in the output layer. In the Topsis model, after declassifying the decision matrix, Shannon's entropy method was used to weigh the criteria and to determine the relative distance from the Euclidean distance. After preparing the models, the study area was analyzed with 970 Km2 with 9 input variables that were converted to raster data into 30x30 pixels. The results of the analysis were mapped with five floors of landslide risk for each model. After applying 5 methods of calculating the error rate to validate the models, it was found that the perceptron neural network model has less error and more adaptation and is better compatible with the geography of the region.
خلاصه ماشینی:
Hydrogeomorphology, No. 24, Vol. 6, Fall 2020, pp 65-82 Hydrogeomorphology, Vol. 6, No. 24, Fall 2020, pp (65-82) Landslide hazard zoning using Artificial Neural Network and TOPSIS models in the downstream of Sanandaj Dam 3 Asadollah Hejazi *1 , Mohammad Hossein Rezaei Moghaddam 2 , Adnan Naseri 1- Associate Professor, Department of Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran 2- Professor, Department of Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran 3- PhD student in Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran Received: 2020/04/14 Final Acceptance: 2020/08/31 Abstract Landslide susceptibility maps are one of the most important tools required for environmental planners and decision-makers, especially in mountainous regions.
The aim of the present research is to investigate and compare two models, the Artificial Neural Network model and the TOPSIS model, in landslide hazard zoning in the downstream area of Sanandaj Dam. To this end, 9 input layers including slope, slope direction, lithology, land use, precipitation, hypsometry, distance from drainage, road, and fault were used in the Arc GIS environment.
Currently, slope instability is a significant form of degradation in the study area, and the aim of the present research is to assess the landslide hazard using two models: Artificial Neural Network (ANN) and TOPSIS.
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