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

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ESLAMI MAHMOOD; SHADFAR SAMAD; MOHAMMADI TORKASHVAND ALI; PAZIRA EBRAHIM

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Journal: JOURNAL OF WATERSHED MANAGEMENT RESEARCH Year:2019 | Volume:10 | Issue:19 Page(s): 117-131

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Title

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

Author(s)

ESLAMI MAHMOOD | SHADFAR SAMAD | MOHAMMADI TORKASHVAND ALI | PAZIRA EBRAHIM | Issue Writer Certificate

Keywords

ANNQ1

Back Propagation Learning AlgorithmQ1

Land UseQ1

Linear ParametersQ2

Perceptron StructureQ1

Abstract

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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APA: Copy

ESLAMI, MAHMOOD, SHADFAR, SAMAD, MOHAMMADI TORKASHVAND, ALI, & PAZIRA, EBRAHIM. (2019). Application of Artificial Neural Network in Study Phenomenon of Landslide and Risk Modeling using Geographic Information System (GIS), Case Study: Alamoot Rood Watershed. JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 10(19 ), 117-131. SID. https://sid.ir/paper/399925/en

Vancouver: Copy

ESLAMI MAHMOOD, SHADFAR SAMAD, MOHAMMADI TORKASHVAND ALI, PAZIRA EBRAHIM. Application of Artificial Neural Network in Study Phenomenon of Landslide and Risk Modeling using Geographic Information System (GIS), Case Study: Alamoot Rood Watershed. JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2019;10(19 ):117-131. Available from: https://sid.ir/paper/399925/en

IEEE: Copy

MAHMOOD ESLAMI, SAMAD SHADFAR, ALI MOHAMMADI TORKASHVAND, and EBRAHIM PAZIRA, “Application of Artificial Neural Network in Study Phenomenon of Landslide and Risk Modeling using Geographic Information System (GIS), Case Study: Alamoot Rood Watershed,” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 10, no. 19 , pp. 117–131, 2019, [Online]. Available: https://sid.ir/paper/399925/en

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