Predictive Modeling of Seismic Events: A Comparative Analysis Of K-Nearest Neighbors and Random Forest Algorithms

Abstract

This study processes and predicts seismic data using data visualization approaches, K-nearest neighbors (KNN) and the random forest (RF) algorithms. The analysis's dataset includes a number of variables connected to earthquakes. The primary goal is to devise a forecast algorithm capable of accurately categorizing seismic events, data visualization tools are utilized to gain insights into the dataset, producing informative charts that depict the distribution and correlations among different variables. This visual evaluation aids in pinpointing anomalies or trends, facilitating a deeper understanding of the data's characteristics and guiding decisions during the modeling phase. Subsequently, the KNN method classifies earthquake occurrences based on their attributes, predicting the class label by considering the characteristics of its nearest neighbors. Additionally, Accurate classification of seismic events is enhanced by using RF, an ensemble learning technique that combines many decision trees to produce predictions. To optimize outcomes, the study adjusts the random forest model's hyperparameters through cross-validation. The study compares the performance of KNN and RF using a confusion matrix. The confusion matrix shows a thorough insight of categorization performance, which provides a comprehensive view of categorization efficacy. This assessment underscores the models' precision and effectiveness in classifying seismic events.