Multivariate Sonar Intelligence via Hybrid Hydro-Acoustic Ensemble Learning Models

Abstract

The ocean environment is a highly complex acoustic space where Sound Navigation and Ranging (SONAR) systems play a crucial role in detecting, classifying, and interpreting underwater signals for defense, geological exploration, and environmental monitoring. In recent years, over 70% of underwater monitoring data has been identified as acoustically rich but noisy, with more than 60% of manually analyzed samples showing inconsistency due to signal overlap and human bias. The need for automated and accurate sound classification and regression modeling arises in applications such as submarine detection, marine geological structure mapping, and industrial underwater noise assessment, where realtime and precise sound source identification is essential. Traditional manual classification methods suffer from high subjectivity, delayed analysis time, and inefficiency in handling large multivariate datasets with overlapping frequency domains. To overcome these limitations, this study introduces a Hybrid Hydro-Acoustics framework that combines regression and classification through an ensemble learning approach. The framework first preprocesses multivariate SONAR data—such as frequency, amplitude, and power spectral density—and then utilizes the ensemble mechanism to integrate decision boundaries and regression estimates from multiple learners. The existing algorithms, including Support Vector Classifier (SVC), Support Vector Regression (SVR) models and Gradient boosting (GB) CART as baseline learners for both regression and classification tasks, while enhancing prediction robustness using an Ensemble Extra Decision Tree (Ensemble EDT) strategy. The proposed Ensemble EDT for Classification effectively captures nonlinear separations in acoustic features, while Ensemble EDT for Regression provides improved prediction accuracy and stability for continuous parameters like sound intensity and frequency response. This hybrid ensemble framework demonstrates superior adaptability and generalization, enabling efficient modeling of complex underwater sound environments with higher accuracy, reduced overfitting, and enhanced computational efficiency.