Comparative Analysis of Logistic Regression, Random Forest, Support Vector Machine, and LSTM Model for Ground Vibration Detection

Ground vibration detection is critical in various domains, including earthquake monitoring, structural health monitoring, and industrial machinery diagnostics. This paper presents a comparative analysis of machine learning and Deep learning models—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-for ground vibration detection. The models are evaluated based on their performance metrics such as MAE, MSE, Accuracy, and Precision. In this paper we have seen that the LSTM-based model shows more accuracy, The results provide insights into the strengths and limitations of each model in handling the complexities of ground vibration data.