Advanced Computing and AI-Driven National Information Systems for Predictive Disaster Risk Management and Economic Loss Mitigation

Abstract

This study addresses the persistent problem that many national disaster management systems remain fragmented, reactive, and weakly integrated despite rising disaster complexity, which limits early warning, coordinated response, and the reduction of economic losses. The purpose of the research was to examine how advanced computing and AI-driven national information systems influence predictive disaster risk management and how this, in turn, supports economic loss mitigation. Using a quantitative, cross-sectional, case-based design, the study drew on 210 valid responses from disaster management officials, ICT and system professionals, emergency response coordinators, policy and planning officers, and analysts involved in national disaster information environments, following 240 distributed questionnaires, 218 returns, and an effective response rate of 87.5%. The key variables were Advanced Computing Capability, AI-Driven Forecasting Capability, National Information System Integration, Real-Time Decision Support, Predictive Disaster Risk Management, and Economic Loss Mitigation. Data were analyzed through descriptive statistics, reliability testing, Pearson correlation, and regression analysis using SPSS. The results showed high mean scores across the main constructs, including Advanced Computing Capability (M = 4.18, SD = 0.64), AI-Driven Forecasting Capability (M = 4.11, SD = 0.69), Predictive Disaster Risk Management (M = 4.14, SD = 0.62), and Economic Loss Mitigation (M = 4.02, SD = 0.71). Reliability was strong, with an overall Cronbach’s alpha of 0.903. Correlation results indicated that Predictive Disaster Risk Management was strongly associated with AI-Driven Forecasting Capability (r = 0.724, p < .01), Real-Time Decision Support (r = 0.701, p < .01), and Economic Loss Mitigation (r = 0.756, p < .01). Multiple regression showed that the four predictors explained 62.8% of the variance in Predictive Disaster Risk Management (R² = 0.628), with AI-Driven Forecasting Capability emerging as the strongest predictor (β = 0.312, p = 0.001). Predictive Disaster Risk Management also explained 57.1% of the variance in Economic Loss Mitigation (R² = 0.571; β = 0.756, p < .001). The study implies that governments should prioritize AI forecasting, integrated national systems, and real-time decision support to strengthen predictive readiness and reduce disaster-related economic disruption.