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

Environmental Pollution, Год журнала: 2025, Номер unknown, С. 125748 - 125748

Опубликована: Фев. 1, 2025

Язык: Английский

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

Energy Storage and Saving, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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

Applied Mathematical Modelling, Год журнала: 2025, Номер unknown, С. 116144 - 116144

Опубликована: Апрель 1, 2025

Язык: Английский

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

Atmosphere, Год журнала: 2025, Номер 16(5), С. 513 - 513

Опубликована: Апрель 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.

Язык: Английский

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

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112626 - 112626

Опубликована: Дек. 1, 2024

Язык: Английский

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