Game Theory-Optimized Attention-Based Temporal Graph Convolutional Network for Spatiotemporal Forecasting of Sea Level Rise

Predicting Sea level rise accurately is crucial in the formulation of effective adaptation plans to counteract the effects of climate change in vulnerable coastal areas, infrastructure, and people. The conventional forecasting models tend to fail in capturing the intricate spatiotemporal relationships affecting sea level variations. In order to overcome the above-mentioned challenges, this research introduces a hybrid predictive model combining a Temporal Graph Convolutional Network (T-GCN) with attention and game theory-based optimization strategy. T-GCN structure is specially tailored to capture spatial dependencies as well as temporal dynamics in sea level change, providing even deeper understanding of the changing dynamics of sea levels. The attention mechanism strengthens the model by dynamically weighing important variables, whereas the game-theoretic optimization efficiently optimizes multiple objectives, e.g., prediction accuracy and robustness. Experimental results, measured in terms of common performance indicators, show the better effectiveness of the proposed model with a correlation coefficient of 0.996512 and an overall error of 0.032154. Through the inclusion of both climatic and socio-economic variables, this methodology provides accurate, data-based insights to inform climate policy and adaptive planning. The results highlight the capabilities of state-of-the-art machine learning methods for solving actual sea level rise challenges.

@article{Swathy2025,
  title     = {Game Theory-Optimized Attention-Based Temporal Graph Convolutional Network for Spatiotemporal Forecasting of Sea Level Rise},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {T M Swathy and K.Ruth Isabels and A. Sindhiya Rebecca and Venubabu Rachapudi and Yousef A.Baker El-Ebiary and Shobana Gorintla and Elangovan Muniyandy},
  doi       = {10.14569/IJACSA.2025.0160857},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160857}
}