Self-Supervised Transformer Model for Intelligent Predictive Disaster Risk Maintenance Through Early Alerts

Abstract

Accurate and timely prediction of natural disasters remains difficult because of complex spatio-temporal dynamics and the heterogeneity of environmental data. This paper presents a Transformer-based multimodal framework for early disaster forecasting that integrates seismic signals, atmospheric parameters, and satellite-derived environmental features. The framework is pretrained using two self-supervised pretext tasks — masked time-series reconstruction and cross-modal contrastive alignment — so that transferable spatio-temporal representations can be learned before the labelled risk-classification head is fine-tuned. A modality-specific encoding and cross-modal attention fusion strategy synchronizes heterogeneous, asynchronously sampled data streams onto a common temporal grid. On the primary evaluation set the fine-tuned model attains 93.5% accuracy and an F1-score of 0.92, and these gains are confirmed using 5-fold cross-validation with paired statistical significance testing $(\mathrm{p}<\text{0. 0 5})$ against CNN, CNN+LSTM, GNN, and two recent Transformer-based baselines (Informer, Patch TST). The model is further evaluated on an independent, temporally and geographically disjoint hold-out dataset to assess generalization, and false-alarm behaviour is analyzed explicitly through the false-alarm rate (FAR) and a cost-sensitive decision threshold. Computational profiling reports parameter count, training time, and per-sample inference latency to establish suitability for real-time deployment. The results indicate that the proposed self-supervised, multimodal Transformer improves detection reliability while reducing false alarms relative to the baselines considered, supporting its use as a component of real-time, resource-aware early-warning systems.