Vol. 2, Issue 2, Part A (2025)

The escalating frequency and severity of natural and human-induced disasters demand rapid, data-driven decision-making frameworks that can adapt to complex and uncertain environments. This study presents an Artificial Intelligence (AI)-enhanced Decision Support System (Artificial Intelligence (AI)-Decision Support System (DSS)) designed to improve disaster management and response through the integration of multi-source data and interpretable machine learning models. The system employs a hybrid deep-learning architecture combining convolutional and transformer-based networks for spatial-temporal analysis, supported by a Bayesian uncertainty module to enhance model transparency and trust. Data from the Copernicus Emergency Management Service, USGS ShakeMap, and EM-DAT databases were used to evaluate the Artificial Intelligence (AI)-Decision Support System (DSS) across flood and earthquake scenarios. Statistical analysis revealed substantial performance improvements over traditional systems, including a 9.3% increase in flood segmentation accuracy, a 5.4% improvement in building-damage classification AUC, and a 10.8-percentage-point gain in decision accuracy. The mean time-to-decision was reduced by 11.3 minutes, while user trust increased by 1.24 points on a seven-point Likert scale. Calibration analysis indicated lower Expected Calibration Error values, reflecting improved reliability in predictive confidence. The results validate the hypothesis that human-in-the-loop, explainable Artificial Intelligence (AI) architectures significantly enhance both decision efficiency and user confidence in high-stakes disaster environments. Furthermore, the study proposes actionable recommendations, including the integration of explainable Artificial Intelligence (AI) dashboards, the development of data-sharing frameworks, and Artificial Intelligence (AI) capacity-building initiatives for emergency personnel. These findings establish that Artificial Intelligence (AI)-driven, ethically governed decision-support tools can accelerate disaster response while ensuring transparency, accountability, and operational trust among multidisciplinary response teams. The proposed Artificial Intelligence (AI)-Decision Support System (DSS) framework represents a scalable, adaptive, and interpretable model for the next generation of technology-assisted disaster management systems.