Using Machine Learning Techniques for Earthquake Prediction Through Student Learning Styles

Abstract

AbstractThe Earthquake is an essential problem in human life, by using machine learning techniques in earthquake prediction, we can save humankind. Using the successful application of machine learning techniques indicates that it would be possible to use them to make accurate forecasts to avoid short-term earthquake damage. In this paper, with the first aim, we have applied seven machine learning techniques, namely, Artificial Neural Network (ANN), Decision Tree (DT), Logistic Regression, Random Forest Classification, Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) to reach the best technique for prediction. The second aim used the methods of two learning styles, surface and deep learning, in training students with programming skills to use the seven techniques. Through two experimental groups, one of them used the method of surface learning (collective), and the other used the method of deep learning (individual).This is to determine the best learning style to teach students programming skills.KeywordsEarthquake predictionArtificial neural network (ANN)Decision treeK-nearest neighbors (KNN)Logistic regressionNaive BayesRandom forestSupport vector machine (SVM)