AI-POWERED FORECASTING MODELS FOR CLIMATE AND ENVIRONMENTAL DATA

Abstract

The growing frequency of extreme climate events, rising global temperatures, and unpredictable environmental changes have intensified the need for accurate climate forecasting systems. Artificial Intelligence (AI)–powered forecasting models offer significant improvements over traditional statistical methods due to their ability to analyze massive datasets, capture nonlinear patterns, and produce high-resolution predictive outputs. This article discusses the role of AI techniques—including deep learning, machine learning, hybrid physics–informed models, and spatiotemporal neural networks—applied to climate and environmental data forecasting. Two graphs illustrate improvements in prediction accuracy and the rising adoption of AI models in climate research. Key challenges such as data scarcity, computational intensity, and interpretability are also highlighted. The article concludes with future directions focusing on AI-enabled early warning systems, integration with remote sensing, and next-generation environmental forecasting infrastructures.