Hong Qiu

EAAI Journal 2026 Journal Article

Corrigendum to “Physics-informed dynamic ensemble learning for real-time urban water quality monitoring” [Eng. Appl. Artif. Intell. 175 (2026) 114628]

• Renfang Wang
• Xinyu Zhao
• Xiufeng Liu
• Xu Cheng
• Hong Qiu

EAAI Journal 2026 Journal Article

Physics-informed dynamic ensemble learning for real-time urban water quality monitoring

• Renfang Wang
• Xinyu Zhao
• Xiufeng Liu
• Xu Cheng
• Hong Qiu

Ensuring high-quality water resources is crucial for sustainable urban development, public health, and resilient city infrastructure, yet traditional anomaly detection methods struggle with the highly variable, non-stationary, and concept-drifting nature of urban water quality data streams. This study proposes a Physics-Informed Dynamic Ensemble Learning (PIDEL) framework, an artificial intelligence approach that combines diverse classical and deep learning models with Physics-Informed Neural Networks (PINNs) embedding convection–diffusion constraints, a Genetic Algorithm (GA) for ensemble optimization, and a Jensen–Shannon Divergence (JSD) based mechanism for dynamic model switching. Applied to a real-world urban water quality dataset, PIDEL achieves an F1-score of 0. 95, representing a 59% improvement over the best static ensemble, while reducing false alarms by 73% compared to traditional methods and maintaining F1-scores above 0. 9 across all sliding windows. The framework processes each 60-minute window in approximately 2. 3 s on standard hardware, demonstrating its suitability for real-time deployment in smart city water systems. These results highlight that integrating physics-informed constraints with dynamic ensemble learning can substantially enhance the reliability, interpretability, and operational value of automated water quality anomaly detection for urban utilities.

AAAI Conference 2026 Conference Paper

TRT: Harnessing Tensor Ring Transformer for Hyperspectral Image Super-Resolution

• Honghui Xu
• Junwei Zhu
• Yubin Gu
• Yueqian Quan
• Chuangjie Fang
• Hong Qiu
• Jianwei Zheng

Deep unfolding networks (DUNs) have recently emerged as a promising approach for hyperspectral image super-resolution (HSISR) by combining the benefits of nonlinear deep learning architectures with interpretable optimization techniques. Despite their advantages, current DUNs face significant challenges, particularly in approximating degradation matrices across both spatial and spectral dimensions, which results in complex and cumbersome model construction. By analyzing the difference between the upsampled low-resolution hyperspectral images (LRHS) and the true target image, we observed that the residual image exhibits strong sparsity, akin to noise. Leveraging this insight, we reformulate the HSISR problem as a robust principal component analysis (RPCA)-based denoising task, effectively eliminating the need for the complex approximation of spatial degradation matrix and its transpose. In addition, we introduce a Tensor Ring Transformer based on multilinear products as the prior term, wherein tokens are mapped to a tensor ring factor domain and the traditional dot product is replaced with a multilinear tensor ring product. This significantly reduces the computational complexity of the Transformer model, from \( \mathcal{O}(N^2d) \) to \( \mathcal{O}(Nr^2) \), with \( r<

EAAI Journal 2025 Journal Article

Enhancing spatiotemporal wind power forecasting with meta-learning in data-scarce environments

• Renfang Wang
• Jingtong Wu
• Xu Cheng
• Xiufeng Liu
• Hong Qiu

Accurate wind power forecasting is critical for maintaining stable power grids, yet the inherent variability of wind and limited data availability for new wind farms present significant challenges. To address these issues, we present a novel artificial intelligence framework that integrates a self-attention enhanced Spatiotemporal Long Short-Term Memory (ST-LSTM) network with Model-Agnostic Meta-Learning (MAML), termed as the Meta-Learning Spatiotemporal Attention Long Short-Term Memory framework (MAML-STALSTM). This deep learning combination enables the model to effectively capture long-range spatiotemporal dependencies while rapidly adapting to new wind farm configurations or changing wind conditions with minimal training data. By employing rigorous data preprocessing techniques and ensuring temporal separation in data splitting, we mitigate potential data leakage and enhance the model’s generalizability. Extensive experiments conducted on both onshore and offshore wind farm datasets demonstrate that our artificial intelligence approach outperforms established baseline models, particularly excelling in data-scarce environments. Ablation studies highlight the crucial roles of the self-attention mechanism and meta-learning in improving forecasting accuracy, adaptation speed, and model robustness. These results emphasize the practical benefits of our approach in enhancing grid stability and supporting the seamless integration of wind energy, thereby contributing significantly to the advancement of sustainable energy solutions.