Climatic and seismic data-driven deep learning model for earthquake magnitude prediction

Deep Learning for Event-Driven Stock Prediction

We propose a deep learning method for eventdriven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6...

Computational Geodynamics for Seismic Hazard Analysis and Earthquake Prediction

Predicting the Maximum Earthquake Magnitude from Seismic Data in Israel and Its Neighboring Countries

This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data w...

Correction: Predicting the Maximum Earthquake Magnitude from Seismic Data in Israel and Its Neighboring Countries.

Tables 1 and 2 appear incorrectly in the published article. Please see the correct Tables 1 and 2, and their captions here. The publisher apologizes for the errors.

Classification of Databases and Methods for Seismic Data Analysis and Earthquake Prediction

Earthquake is one the most important disasters in the world. In order to save lives and building substructures of countries, more research in this field should be carried out as a matter of severity. Computer modeling and different artificial intelligence algorithms are known as applicable tools for the earthquake hazards prediction and prevention. This article tries to review the recent studie...