Xiuli Yu, Peng Du, Journal of Information Processing Systems Vol. 21, No. 3, pp. 296-307, Jun. 2025  

https://doi.org/10.3745/JIPS.04.0350
Keywords: Cooperative Early Warning, Ensemble learning, Multi-Feature Fusion, Multi-Model Fusion
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Abstract

Enterprises in the coastal regions of China release abundant pollutants that have considerably deteriorated the air quality. To address this issue, an information fusion technology has been proposed herein for predicting coastal air quality in Liaoning Province. To this end, real-time data analysis of water, air, and soil pollutants emitted from diverse coastal enterprises was performed using a multi-model selection strategy for ensemble learning. This approach integrated meteorological information and considered the unique learning principles and observational disparities among various algorithms. The proposed approach explored the influence of collaborative early warning of multi-feature pollution source emissions on the surrounding environment. By combining the base learner and meta-learner in the multi-model fusion strategy, the ensemble model yielded better prediction results, particularly using strong learners at the primary level and linear mode at the secondary level. This optimal combination strategy was used to develop a collaborative monitoring and early warning model, which incorporated multi-feature data from water, air, and soil sources in the coastal environment. This multi-feature collaboration enhanced the prediction accuracy compared with that of single-feature models and further amplified the early warning capabilities enabled by multi-model fusion.

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