A MACHINE LEARNING MODEL FOR AN EARTHQUAKE FORECASTING USING PARALLEL PROCESSING

Earthquake is a devastating natural hazard which has a capability to wipe out thousands of lives and cause economic loss to the geographical location. Seismic stations continuously gather data without the necessity of the occurrence of an event. The gathered data is processed by the model to forecast the occurrence of earthquakes. This paper presents a model to forecast earthquakes using Parallel processing. Machine Learning is rapidly taking over a variety of aspects in our daily lives. Even though Machine Learning methods can be used for analyzing data, in the scenario of event forecasts like earthquakes, performance of Machine Learning is limited as the data grows day by day. Using ML alone is not a perfect solution for the model. To increase the model performance and accuracy, a new ML model is designed using parallel processing. The drawbacks of ML using central processing unit (CPU) can be overcome byGraphic Processing unit (GPU) implementation, since the parallelism is naturally provided using framework for developing GPU utilizing computational algorithms, known as the Compute Unified Device Architecture (CUDA). The implementation of hybrid state vector machine (H-SVM) algorithm using parallel processing through CUDA is used to forecast earthquakes. Our experiments show that the GPU based implementation achieved typical speedup values in the range of 3-70 times compared to conventional central processing unit (CPU). Results of different experiments are discussed along with their consequences.