Earthquake Forecasting Using Optimized Levenberg–marquardt Back-propagation Neural Network

Abstract

In this study, an effective earthquake forecasting model is introduced using a hybrid metaheuristic machine learning (ML) algorithm with CUDA-enabled parallel processing. To improve the performance and accuracy of the model, a novel hybrid ML model is developed that utilizes parallel processing. The model consists of a Chaotic Chimp based African Vulture Optimization Algorithm (CCAVO) for feature selection and a Hybrid Levenberg-Marquardt Back-Propagation Neural Network (HLMt-BPNN) for prediction. The proposed model follows a four-step process: preprocessing the raw data to identify seismic indications, extracting features from the preprocessed data, using optimized ML algorithms to forecast the earthquake and its expected time, epicenter, and magnitude, and implementing the model using the Python platform. The model's performance is evaluated using various criteria, including accuracy, precision, recall, F-measure, specificity, false negative ratio, false positive ratio, negative prediction value, Matthew’s correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error. The proposed model achieved an accuracy of 98%, which is higher than the accuracy of existing earthquake prediction methods.