Machine learning-driven classification of natural disasters via parallel confidence fusion

Abstract The low-frequency characteristics of infrasound signals enable them to play a critical role in the long-range detection of disasters such as earthquakes, landslides, and chemical explosions. However, the accurate classification of disaster types based on infrasound signals remains a significant challenge. Traditional methods rely on manual feature extraction, often failing to capture spatiotemporal patterns. While deep learning approaches, particularly convolutional neural networks (CNNs), show promise, they are limited by finite depth, inadequate temporal modeling capabilities, and network degradation. Additionally, a single CNN struggles to learn diverse feature representations and exhibits low fault tolerance to anomalies. To address these challenges, a machine learning-driven classification method is proposed featuring three synergistic components: a synergistic feature-deep learning architecture that integrates continuous wavelet-scale average coefficients-based time-scale transformation and deep feature learning to enhance discriminative capability; a triple-branch architecture that integrates multi-scale spatial convolutions and adaptive temporal regulation to model propagation dynamics; and a parallel confidence fusion module that dynamically weights branch outputs for final robust decisions. Experimental results show that the proposed method achieves 98.75% accuracy on a natural earthquake infrasound dataset and maintains a high accuracy on an open-source multi-class dataset, demonstrating strong robustness and generalization capability for natural disaster monitoring.