Prediction of latitude and longitude of earthquakes at global level using the regressive objective regression method

<p>Ricardo Oses Rodriguez, Carmenate Ramirez Anai, Alfredo Pedraza Martinez and Rigoberto Fimia Duarte</p>

Prediction of latitude and longitude of earthquakes at global level using the regressive objective regression method

Ricardo Oses Rodriguez, Carmenate Ramirez Anai, Alfredo Pedraza Martinez and Rigoberto Fimia Duarte

CMP Villa Clara, Cuba

: J Phys Res Appl

Abstract

Objective: To model the series of data of a set of earthquakes at a global level included in the period from 2014-08-27 23.22.23 UTC until 2014-08-27 04.47.36 UTC, there were 50 earthquakes taken globally and it was determined a model to predict the longitude and latitude of these events. Methods: The Regression Objective Methodology, ROR, is used. Two models are calculated, the first for latitude and the second for longitude, the models are determined in a short term. Results: The correlation coefficients are obtained between the real value and the forecast of 0.716 for model 1 with an error of 25.09 significant degrees at 99% and, for model 2, R = 0.637 with an error of 74.64 degrees significant at 90%. Earthquakes for latitude depends on the value 1.16.12 earthquake behind, the parameter returned 16 steps back are significant at 99%. For this model the trend is 0.118 not significant, model 2 depends on 1, 2, 11, 21 steps back, earthquakes 1 step back is significant at 90%, the trend of the series for model 2 is a rise in length by 4,637 significant at 95%. Discussion: Our work shows the longitude and latitude of earthquakes occurrence are perfectly predictable parameters similar to the variable wind, so a careful attention must be paid to the prevention planes of these phenomena to reduce vulnerability in cities. Conclusions: It is concluded that model 1 is the least error while trend in model 2 has a significant increasing. Global earthquakes are regressive events at planet level and what happens in one place has repercussions in another, not randomly or due to chance, but rather a well-determined phenomenon.