NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization

An Earthquake Early Warning System (EEWS) can save lives. It can also be used to manage the critical lifeline infrastructure and essential facilities. Recent research on earthquake prediction towards the development of an EEWS can be classified into two groups based on a) arrival of P waves and b) seismicity indicators. The first approach can provide warnings within a timeframe of seconds. A seismicity indicator-based EEWS is intended to forecast major earthquakes within a time frame of weeks. In this paper, a novel seismicity indicator-based EEWS model, called neural EEWS (NEEWS), is presented for forecasting the earthquake magnitude and its location weeks before occurrence using a combination of a classification algorithm (CA) based on machine learning concepts and a mathematical optimization algorithm (OA). The role of the CA is to find whether there is an earthquake in a given time period greater than a predefined magnitude threshold and the role of the OA is to find the location of that earthquake with the maximum probability of occurrence. The model is tested using earthquake data in southern California with a combination of four CAs and one OA to find the best EEWS model. The proposed model is capable of predicting strong disastrous events as long as sufficient data are available for such events. The paper provides a novel solution to the complex problem of earthquake prediction through adroit integration of a machine learning classification algorithm and the robust neural dynamics optimization algorithm of Adeli and Park.