Geospatial AI for Climate Forecasting: A Deep Learning and EViews-Integrated Approach Using ArcGIS and Python

Abstract: This study presents an integrative framework that combines geospatial artificial intelligence (GeoAI), deep learning, and econometric modeling to enhance the accuracy of climate forecasting. Leveraging ArcGIS and Earth Engine for spatial data preprocessing, Long Short-Term Memory (LSTM) networks in Python are trained on multi-temporal climate datasets to predict solar and wind energy availability. The predictive outputs are statistically validated using Vector Autoregression (VAR) and cointegration analysis in EViews, ensuring robustness and interpretability. Results indicate that incorporating spatial heterogeneity significantly improves forecast precision compared to conventional time-series models. The proposed hybrid framework supports scalable, real-time, and policy-relevant climate forecasting, providing critical insights for energy infrastructure planning and sustainable resource management. Keywords: Geospatial AI, Climate Forecasting, ArcGIS, LSTM, Python, EViews, Deep Learning, Renewable Energy Prediction, Econometric Modeling, Spatial Data Analysis