Joint Classification of Haze and Dust Events Using Factorial Hidden Markov Model Framework

TL;DR

This paper introduces a scalable factorial hidden Markov model framework for joint classification of haze and dust pollution events, effectively capturing complex interactions and nonlinear correlations in environmental data.

Contribution

It develops a novel factorial hidden Markov model with Walsh-Hadamard transform and Gaussian copula, improving computational efficiency and modeling accuracy for pollution event classification.

Findings

Achieved a Micro-F1 score of 0.9459 in empirical tests.

Significant F1-score improvements for low-frequency pollution classes.

Provides a scalable modeling approach for complex environmental event classification.

Abstract

Haze and dust pollution events have significant adverse impacts on human health and ecosystems. Their formation-impact interactions are complex, creating substantial modeling and computational challenges for joint classification. To address the state-space explosion faced by conventional Hidden Markov Models in multivariate dynamic settings, this study develops a classification framework based on the Factorial Hidden Markov Model. The framework assumes statistical independence across multiple latent chains and applies the Walsh-Hadamard transform to reduce computational and memory costs. A Gaussian copula decouples marginal distributions from dependence to capture nonlinear correlations among meteorological and pollution indicators. Algorithmically, mutual information weights the observational variables to increase the sensitivity of Viterbi decoding to salient features, and a single global weight hyperparameter balances emission and transition contributions in the decoding objective. In an empirical application, the model attains a Micro-F1 of 0.9459; for the low-frequency classes Dust prevalence below 1\% and Haze prevalence below 10\%, the F1-scores improve from 0.19 and 0.32 under a baseline FHMM to 0.75 and 0.68. The framework provides a scalable pathway for statistical modeling of complex air-pollution events and supplies quantitative evidence for decision-making in outdoor activity management and fine-grained environmental governance.