Application of Artificial Neural Network in Study Phenomenon of Landslide and Risk Modeling using Geographic Information System (GIS), Case Study: Alamoot Rood Watershed

نویسندگان

  • eslami, mahmood Department of Pedology, Science and Research Branch, Islamic Azad University, Tehran
  • pazira, ebrahim Department Pedology, Science and Research Branch, Islamic Azad University, Tehran
  • shadfar, samad Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran
چکیده

     One of the natural disasters that occurs in abundance in Iran, due to the geological structure, morphological and seismic conditions, and damages the lives and property of people is a landslide. Roodbar Alamoot watershed in the east of Qazvin province is a mountainous region with a high potential for occurrence of landslides. Because of their active status, there is also a growing trend of landslide occurrence and damage to rangeland, agricultural lands and residential areas. In this research, landslide survey was conducted using Artificial Neural Network model (ANN). Soil, geology, slope, aspect, elevation classes, linear parameters including distance from the river, distance from the fault, distance from the road, sensitivity of the rocks to erosion, rainfall and land use as factors affecting landslide. Using artificial neural network model with the multiple-layer perceptron structure and back propagation learning algorithm, landslide hazard zonation was performed. The results showed that the arrangement of 11-7-1 with active sigmoid function is the best structure for studying the phenomenon of landslide in this study area. The training, test and validation of the model were performed with 15, 15 and 75 Percentage of data that randomly selected. After optimizing the network structure, standardized information was provided to the network. Based on the results of landslide hazard zonatin with Artificial Neural Network model, respectively, 6.2, 10.7, 17.1, 64.3 and 5.3 percent of the area placed in the very low, low, moderate, high and very high risk classes. The network has 0.5 learning ratio, 7 neurons in the hidden layer and the least amount of error in the experiment (RMSe = 0.0321)  

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عنوان ژورنال:

دوره 10  شماره 19

صفحات  117- 131

تاریخ انتشار 2019-05

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