Earthquakes magnitude predication using artificial neural network in northern Red Sea area

Abstract

Since early ages, people tried to predicate earthquakes using simple observations such as strange or atypical animal behavior. In this paper, we study data collected from past earthquakes to give better forecasting for coming earthquakes. We propose the application of artificial intelligent predication system based on artificial neural network which can be used to predicate the magnitude of future earthquakes in northern Red Sea area including the Sinai Peninsula, the Gulf of Aqaba, and the Gulf of Suez. We present performance evaluation for different configurations and neural network structures that show prediction accuracy compared to other methods. The proposed scheme is built based on feed forward neural network model with multi-hidden layers. The model consists of four phases: data acquisition, pre-processing, feature extraction and neural network training and testing. In this study the neural network model provides higher forecast accuracy than other proposed methods. Neural network model is at least 32% better than other methods. This is due to that neural network is capable to capture non-linear relationship than statistical methods and other proposed methods.