Predicting the Earthquake Magnitude Using the Multilayer Perceptron Neural Network with Two Hidden Layers

Jamal Mahmoudi (Structural Engineering Research Center,International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran)
Mohammad Ali Arjomand (Assistant Professor, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran)
Masoud Rezaei (Faculty of Earthquake Engineering, Road-Building and Housing Research Center, Tehran, Iran)
Mohammad Hossein Mohammadi (Faculty of Civil Engineering, Kharazmi University, Tehran, Iran)

Civil Engineering Journal
Vol 2, No 1 (2016): January

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.