Earthquake prediction using seismic bumps with Artificial Neural Networks and Support Vector Machines

Earthquakes are what happens when immediate vibrations which shake earth surface, spread as waves as a result of earth crust cracks. Earthquakes depend on variables such as the way of spreading of these waves, calculation of these waves and calculating methods, evaluations of these recorded data sets. Predicting probable earthquakes and minimizing the damages are the important factors. Decision systems can be developed only through using seismic bump data. At this point, seismic data will be classified first and then comparative results will be analyzed at the test stage. We use end seismic data obtained from mine pit which are classified through classification algorithms. Artificial Neural Networks and Support Vector Machine are used in the classification. Early detection rate is calculated as 83% with the classification through Artificial Neural Network. Early detection rate is calculated as 91% with the classification through Support Vector Machine.