Ensemble Machine Learning for Multivariate Sonar Signal Classification

Abstract

Underwater acoustic monitoring plays a vital role in marine research, environmental conservation, and vessel detection. However, the rapid growth of sonar data generated by modern sensing technologies has created a strong demand for automated, accurate, and scalable analysis systems. Conventional sonar analysis relies heavily on manual interpretation and isolated machine learning models, leading to inefficiencies, inconsistent results across varying environments, and limited real-time applicability. These challenges highlight the need for a unified framework capable of handling the complete analytical pipeline in a streamlined and user-friendly manner. This study introduces a Unified MultiModel Learning Platform designed for intelligent classification and regression of SONAR signals in complex marine acoustic environments. The proposed system integrates end-to-end functionalities, including data preprocessing, feature scaling, dataset partitioning, model training, evaluation, comparison, visualization, and real-time prediction. Central to the framework is a novel hybrid ensemble model based on Classification and Regression Tree (CART) principles, combining Decision Tree and Extra Trees algorithms through a voting mechanism, referred to as the Ensemble EDT model. To ensure robust benchmarking, additional models such as Gradient Boosting and Support Vector Machine are incorporated for comparative performance evaluation. Experimental findings indicate that the Ensemble EDT model achieves superior classification accuracy with reduced misclassification rates and demonstrates strong regression performance with high predictive alignment. The integrated CART-based ensemble approach enhances model stability and generalization in noisy underwater environments. Furthermore, the platform provides a web-based interface supporting both individual and batch predictions, offering an efficient, scalable, and practical solution for advanced underwater acoustic signal analysis.