Transformers-Based Multimodal Deep Learning for Real-Time Disaster Forecasting and Adaptive Climate Resilience Strategies

Abstract

Real time forecasting of disasters needs to be advanced and easy because with increasing disasters their frequency and severity. Traditional prediction can only be made with traditional disaster prediction methods: numerical weather prediction (NWP) models and remote sensing techniques, which are computationally inefficient, data sparse and cannot adapt to dynamic environmental changes. In order to overcome these limitations, this research presents a Transformer Based Multimodal Deep Learning Model to combine the existing multiple data sources ranging from satellite imagery, IoT sensor networks, meteorological observations etc., to meteorological and social media analytics. The model employs a multimodal fusion strategy, enabling dynamic feature selection and seamless integration of heterogeneous data streams. In contrast to the conventional deep learning techniques, such as CNNs and LSTMs, the transformer based model has excellent ability towards long-range dependency, reducing the latency of light inference and better computational efficiency. The results are proven to be 94% accurate, 91% precise and has 40% reduction in inferencer latency in real time, which makes it suitable for disaster forecasting. The advancement of the multimodal deep learning methodologies presents this research as one which serves to contribute to the AI driven disaster resilience. We will also work on future work in the form of advanced transformer variants, more data integration, and explainable AI (XAI) techniques for model interpretability and scalability. Finding have implications for the transformative potential of AI in climate adaptation and serve as a robust foundation for the next generation early warning systems and climate adaptation disaster risk mitigation across multiple sectors.