Cited by Air Quality Index Prediction through TimeGAN Data Recovery and PSO-Optimized VMD-Deep Learning Framework

A PM2.5 spatiotemporal prediction model based on mixed graph convolutional GRU and self-attention network DOI

Zhao Guyu,

Xiaoyuan Yang,

Shi Jiansen

et al.

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 125748 - 125748

Published: Feb. 1, 2025

Language: Английский

Citations

State of Charge Estimation of Lithium-ion Batteries in an Electric Vehicle using Hybrid Metaheuristic - Deep Neural Networks Models DOI

Zuriani Mustaffa, Mohd Herwan Sulaiman, Jeremiah Isuwa

et al.

Energy Storage and Saving, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

A grey incidence model with cumulative time-delay effects and its applications DOI

Jing Sun, Yaoguo Dang, Shengxiang Yang

et al.

Applied Mathematical Modelling, Journal Year: 2025, Volume and Issue: unknown, P. 116144 - 116144

Published: April 1, 2025

Language: Английский

Citations

Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation DOI

Yue Hu, Yongkun Ding, Wenjing Jiang

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(5), P. 513 - 513

Published: April 28, 2025

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of across diverse geographical climatic regions, this study proposes novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Quality Index (AQI) time-series prediction. Through systematic analysis multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover climate zones (subtropical temperate), gradients (coastal inland), topographical variations (plains mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics observational data, providing statistical justification implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation extended beyond conventional single-city approaches, demonstrating model generalizability distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance 23.6–59.6% reduction Root-Mean-Square Error (RMSE) relative baseline LSTM models, along consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed exhibited robust generalization capabilities divisions (R2 = 0.92–0.99), establishing reliable decision-support platform regionally adaptive early-warning systems. This provides valuable insights addressing spatial heterogeneity modeling applications.

Language: Английский

Citations

Air Quality Index Prediction through TimeGAN Data Recovery and PSO-Optimized VMD-Deep Learning Framework DOI

Kenan Wang,

Tianning Yang,

Shanshan Kong

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112626 - 112626

Published: Dec. 1, 2024

Language: Английский

Citations