Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification

--Effective disaster management requires timely and accurate insights, yet traditional methods struggle to integrate multimodal data such as images, weather records, and textual reports. T o address this, we propose DisasterNet-LLM, a specialized Large Language Model (LLM) designed for comprehensive disaster analysis. By leveraging advanced pretraining, cross-modal attention mechanisms, and adaptive transformers, DisasterNet-LLM excels in disaster classification. Experimental results demonstrate its superiority over state-of-the-art models, achieving higher accuracy of 89.5%, an F1 score of 88.0%, AUC of 0.92%, and BERTScore of 0.88% in multimodal disaster classification tasks. Disasters, both natural and human-made, have increasingly devastating consequences that affect millions of lives, disrupt economies, and damage critical infrastructure [1, 2].