TRANSFORMER MODELS FOR CLIMATE CHANGE AND SUSTAINABILITY EVALUATION

Transformer models are emerging as practical solutions for climate change and ecological issues. Their ability to spot patterns and correlations in massive amounts of environmental data collected from sensors, satellites, and reports allows them to outperform earlier approaches. Their ability to document climatic patterns over extended periods makes them invaluable for risk assessment and real-time monitoring of changes. These models' attention processes aid in drawing attention to the most crucial aspects, making predictions more comprehensible and transparent. When compared to previous machine learning techniques, transformers perform better when trying to model intricate, nonlinear relationships among climatic, ecological, and socioeconomic variables. Their adaptability makes them useful in both local and global systems, providing trustworthy data for planners' long-term strategies. To give towns extra time to prepare for severe weather, they also improve early warning systems. Extensive testing on many datasets has confirmed the accuracy and reliability of these models. Finally, a data-driven, scalable, and up-to-date approach to guiding climate action is offered by intelligence based on transformers. It is a significant advancement in the use of AI to create more robust and long-lasting structures.