Big Data Analytics for Climate and Atmospheric Monitoring
This research focuses on utilizing big data analytics, machine learning, and transformer models for fire detection in the context of climate and atmospheric monitoring. The objective is to develop innovative techniques that leverage large-scale climate and atmospheric datasets to identify and predict fire occurrences. Specialized machine learning algorithms and transformer models will be employed to learn patterns and capture spatial and temporal dependencies within the data. The research also addresses data preprocessing challenges and emphasizes the importance of feature engineering for accurate fire detection. The expected outcome is an efficient and accurate fire detection system that enhances early warning systems and aids in proactive firefighting efforts. This research contributes to the field of big data analytics for climate and atmospheric monitoring by advancing our ability to detect and predict wildfires, ultimately facilitating effective disaster management and mitigation strategies.