<![CDATA[Environmental monitoring has become increasingly critical as climate change continues to pose significant global challenges, impacting ecosystems, economies, and human health. Predicting and managing these impacts requires advanced technological solutions, and Artificial Intelligence (AI) has emerged as a powerful tool in this domain. This study aims to explore the integration of AI techniques, such as machine learning and deep learning, into environmental monitoring to enhance the accuracy of climate change impact predictions and improve management strategies. The methods employed include the application of Convolutional Neural Networks (CNN) for land cover classification and Long Short-Term Memory (LSTM) models for forecasting air quality levels. The results indicate that AI significantly improves prediction accuracy, with CNN achieving high performance in land classification and LSTM models providing reliable forecasts for air quality changes. The findings suggest that AI can be instrumental in transforming environmental monitoring, enabling more proactive and data-driven climate change management. Future research should focus on improving data quality, model interpretability, and expanding AI applications in various environmental contexts.]]>
Copyrights © 2024
