Leveraging Hybrid AI Models for Advanced Strategic Forecasting in E-Governance and Smart Urban Planning Systems

The work proposes a new hybrid model integrating Spatial-Temporal Graph Convolutional Networks (ST-GCN) and Transformer architecture to solve multifaceted spatio-temporal forecasting problems for smart city usage. The approach takes advantage of ST-GCN's ability to handle spatial interdependence among interrelated sensor nodes and the Transformer's long-range temporal correlation modeling power using multi-head self-attention mechanisms. The research employs an extensive Smart City dataset of sensor readings collected over time from various urban sites to forecast essential parameters like traffic flow and environmental conditions. Execution is performed in Python, allowing for effective model training and assessment with powerful deep learning libraries. The ST-GCN + Transformer hybrid model is intended to handle graph-structured input data, with the sensor nodes constituting the vertices and their interconnections represented in an adjacency matrix, along with temporal series of multivariate features. This design does a great job of incorporating both spatial and temporal aspects and does so without the limitations inherent in methods currently in place that tend to manage them individually. Experimental results show that the proposed approach obtains a mean absolute error (MAE) of 6.8, root mean squared error (RMSE) of 11.1, and mean absolute percentage error (MAPE) of 7.8%, which outperforms state-of-the-art baselines ARIMA, LSTM, independent ST-GCN, and Transformer networks. In comparison with these baselines, the hybrid model enhances prediction accuracy by about 15-30%, which clearly indicates its superior performance in capturing intricate urban dynamics. Finally, this work makes an important contribution in the form of a strong forecasting system adapted for smart city data, offering improved accuracy and credibility critical for city planning and management. The encouraging outcomes recommend further investigation into coupled graph-based and attention-based models in various spatio-temporal forecasting tasks.