  In this study we determined neural network weights and biases by Imperialist
Competitive Algorithm (ICA) in order to train network for predicting earthquake
intensity in Richter. For this reason, we used dependent parameters like
earthquake occurrence time, epicenter's latitude and longitude in degree, focal
depth in kilometer, and the seismological center distance from epicenter and
earthquake focal center in kilometer which has been provided by Berkeley data
base. The studied neural network has two hidden layer: its first layer has 16
neurons and the second layer has 24 neurons. By using ICA algorithm, average
error for testing data is 0.0007 with a variance equal to 0.318. The earthquake
prediction error in Richter by MSE criteria for ICA algorithm is 0.101, but by
using GA, the MSE value is 0.115.
