AI-Enhanced Atmospheric Data Fusion for Real-Time Climate Anomaly Detection and Prediction

It also emerges that climatic anomalies are increasing at an alarming rate, and the current demand is for advanced prediction systems that can ‎detect anomalies as they occur and respond to them. Incorporating data fusion methods with AI, this study proposes an original Adaptive ‎Atmospheric Fusion Network (AAFN) to assist in enhancing the discovery of climate change and prediction. To make the data on climate ‎more representative, the proposed system will incorporate a Recurrent Temporal Fusion Network (RSTFN) to combine data from multiple ‎sources, including IoT sensors, satellite images, and meteorological stations. To improve feature extraction and the prediction of extreme ‎weather conditions with greater precision, a Dual Attention LSTM Transformer (DAL-T) model is also employed. Lightweight AI models ‎are deployed on edge computing nodes to provide instantaneous anomaly alerts, minimizing latency and enabling quicker decision-making. ‎The system also employs Federated Learning, applying it to collaboratively train the model using decentralized data sources, thereby ‎strengthening the models while preserving the privacy of the data. The application of XAI methods enables an AI-based visualisation ‎dashboard to provide meteorologists and policymakers with transparency on a list of actionable insights, enabling them to respond to climate ‎risks accordingly. It has been experimentally demonstrated that AAFN can identify climate anomalies with an accuracy of up to 98%, ‎surpassing existing models in terms of response time and Accuracy. This novel approach aims to mitigate climate risk faced by organizations ‎through real-time prediction and environmental resilience‎.