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

Abstract

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.