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Masoud Rezaei
Faculty of Earthquake Engineering, Road-Building and Housing Research Center, Tehran, Iran
Civil Engineering, Building, Construction & Architecture

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Predicting the Earthquake Magnitude Using the Multilayer Perceptron Neural Network with Two Hidden Layers Jamal Mahmoudi; Mohammad Ali Arjomand; Masoud Rezaei; Mohammad Hossein Mohammadi
Civil Engineering Journal Vol 2, No 1 (2016): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/cej-2016-00000008

Abstract

Because of the major disadvantages of previous methods for calculating the magnitude of the earthquakes, the neural network as a new method is examined. In this paper a kind of neural network named Multilayer Perceptron (MLP) is used to predict magnitude of earthquakes. MLP neural network consist of three main layers; input layer, hidden layer and output layer. Since the best network configurations such as the best number of hidden nodes and the most appropriate training method cannot be determined in advance, and also, overtraining is possible, 128 models of network are evaluated to determine the best prediction model. By comparing the results of the current method with the real data, it can be concluded that MLP neural network has high ability in predicting the magnitude of earthquakes and it’s a very good choice for this purpose.
Co-Authors Jamal Mahmoudi Mohammad Ali Arjomand Mohammad Hossein Mohammadi