Real-time data stream learning for emergency decision-making under uncertainty.

As one factor resulting in health emergency, climate change has received high attention in recent years. Real-time identifying and tracking the potential climate-related risk becomes more challenging due to the changeable and evolving data under uncertainty. To enhance risk early warning and decision-making, this paper proposes a self-adaptive machine learning framework based on real-time data stream learning, called C-SA-Stacking, for risk prediction. First, a stacking-based ensemble model for data stream learning is built with a meta-model embedded, which can self-update selectively and dynamically. Second, an online learning scheme is added to this framework for real-time adaptation. Finally, a mapping of correlations between data streams is constructed to further enhance the learning performance. By testing the framework on six synthetic datasets and six real-world datasets with different risk change scenarios and conducting comparative experimental analysis, the experimental results show that the prediction error is reduced as respected, indicating the effectiveness and real-time of the proposed framework for risk prediction under uncertainty. The results discussion reflects the framework can be applied to real-world scenarios to help identify and track the risk of climate change-caused health emergencies for decision-making. • A stacking-based ensemble learning model with a self-adaptive meta-model for data stream learning is constructed. • A real-time updating process of meta model is proposed to improve the prediction accuracy of the learning model. • A correlation mapping process to help correct and enhance the prediction results is embedded to maintain accuracy. • The proposed algorithm is tested under both synthetic and real-world scenarios with detailed discussion. [ABSTRACT FROM AUTHOR]