Article
AN AI-DRIVEN CLIMATE MONITORING FRAMEWORK BASED ON PREDICTIVE AND OPTIMIZATION MODELS
Climate change has intensified the need for reliable and intelligent climate monitoring systems capable of analyzing large and complex environmental datasets. Conventional monitoring approaches often struggle with data heterogeneity, limited automation, and weak forecasting capacity, which restrict their effectiveness in supporting climate-related decision-making. This study develops an AI-driven climate monitoring framework that brings together predictive modelling and optimisation methods within a unified analytical system. The framework employs machine learning and time-series prediction techniques to forecast temperature variations and emission patterns, while optimisation models are used to improve the efficiency and performance of monitoring and energy-related processes. The proposed approach is evaluated using climate datasets under an experimental setting, and the results indicate improvements in both prediction accuracy and operational efficiency when compared with baseline models. The study demonstrates how AI-enabled predictive and optimisation capabilities can strengthen climate monitoring practices and contribute to data-informed environmental planning and sustainability initiatives
Full Text Attachment
