MADGCN: A Meteorology-Aware Spatio-Temporal Graph Convolution Network for Long-Term Air Pollution Forecasting

MeteoGST: Meteorology-Driven Spatiotemporal Graph Learning for Epidemic Influenza Forecasting

Accurate influenza forecasting is essential for public health emergency preparedness and timely resource allocation. Although meteorological factors are established modulators of influenza transmission, existing deep-learning models rarely exploit this physical knowledge in a principled way. We introduce MeteoGST, a meteorology-driven spatiotemporal graph learning framework that combines (i) multi-scale feature extraction across seven operational meteorological variables (T_max, T_min, DTR, absolute humidity q, relative humidity RH, surface-pressure anomaly p_anom, and 10 m wind speed U10) via parallel dilated convolutions, TCN, and Transformer branches; (ii) a meteorology-aware dynamic graph attention network (MeteoGAT) whose edges blend geographic adjacency with time-varying meteorological similarity through a learned gate α; (iii) residual-trend decomposition with a peak-aware composite loss; and (iv) anti-smoothing meta-learning adaptation. On a 34-city pre-COVID-19 benchmark (2018–2019), MeteoGST achieves an RMSE of 0.65/0.85/1.12, MAE of 0.48/0.63/0.82, R2 of 0.84/0.76/0.70, and Peak F1 of 0.72/0.65/0.59 at 7-, 14-, and 30-day horizons, respectively—improvements of 4–6% over the strongest GNN baseline (MPNN-LSTM) and 31–44% over classical baselines (ARIMA/LSTM). Under a 2021–2022 distribution-shift stress test, the model retains its ranking at 1- and 4-week horizons (PCC 0.78/0.62) and degrades gracefully at 8 weeks, demonstrating robustness beyond the training distribution. MeteoGST offers an operationally deployable tool (MeteoGST-Lite: ≈1 ms per city-week on a laptop-class CPU) for integrated meteorology-aware influenza surveillance.

Spatio Temporal Graph Neural Network for Accurate Climate Prediction using Multivariate Data

Climate prediction plays a critical role in environmental monitoring, disaster preparedness, agricultural planning, and sustainable resource management. Existing forecasting approaches primarily focus on either temporal sequence learning or spatial feature extraction, which limits their capability to capture the complex interdependence among climatic variables distributed across geographical regions and time. To overcome this limitation, this study proposes a novel Spatio-Temporal Graph Neural Network (ST-GNN) framework that jointly models spatial relationships and temporal climate dynamics within a unified deep learning architecture. The novelty of the proposed work lies in integrating graph-based spatial representations with temporal neural learning to simultaneously capture geographical connectivity and evolving climatic patterns for multivariate climate forecasting. In addition, the framework introduces a unified spatio-temporal learning mechanism capable of handling interconnected environmental variables efficiently. Experimental evaluation demonstrates the effectiveness of the framework with a Mean Absolute Error (MAE) of 7.6050% and a Root Mean Squared Error (RMSE) of 14.8201%. The model achieved 99.52% classification accuracy with precision, recall, and F1-score values of 98.79%. Multivariate climate data collected from a Kaggle dataset were used for training and evaluation.

Deep Learning-based Air Quality Forecasting System for Urban India Using Temporal Neural Networks

Air pollution poses a serious hazard to public health in Cities of India; Among all sources, those resulting from Energy Consumption dominate the deaths caused by air pollution. Economic growth has intensified air pollution and climate change issues at the same time. Existing prediction methods focus on isolated single-site time-series forecasting, ignoring spatial dependencies and cross-city pollution transport, limiting regional management effectiveness. Based on the hourly air quality data of key pollutants in seven cities across India during 2015-2020, this paper first explores their concentrations and correlations; Then it builds a Deep Learning forecasting system consisting of Long Short-Term Memory (LSTM), a convolutional neural network, and long short-term memory networks (CNN-LSTM) and a transformer to investigate its AQI-prediction performance. LSTM can capture long-term temporal dependencies; CNN-LSTM combines spatial-temporal information model integration; Transformers explore long-range relationships through attention mechanisms. Based on the experimental results of this paper, it can be concluded that CNN-LSTM is more effective than other systems. It also has some advantages over single-LSTM and the transformer.

Building Urban Traffic Flow Prediction Model Using Spatio-Temporal Graph Convolutional Networks

The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.

Robustness of Spatio-Temporal Graph Neural Networks for Traffic Forecasting Under Realistic Sensor Degradation

A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.

Mgstfn: multi-granularity spatio-temporal-frequency network for traffic flow forecasting