"background": "Process-control systems in industrial and infrastructure sectors are critical for operational safety and efficiency. In many developing economies, systematic risk assessment and forecasting for these systems remain underdeveloped, leading to preventable failures and economic losses. ", "purpose and objectives": "This study aimed to develop and evaluate a methodological framework for assessing process-control system risks. Its core objective was to construct a robust time-series forecasting model to predict risk trajectories and quantify potential reductions. ", "methodology": "A hybrid methodological framework was employed, integrating historical failure data analysis with statistical forecasting. The core forecasting model is an autoregressive integrated moving average (ARIMA) model with exogenous variables (ARIMAX), specified as \ (B) \ᵈ yt = \ (B) \ + =1^{k\ xi, t, where yt is the risk index. Model parameters were estimated using maximum likelihood, and forecasts were generated with 95% confidence intervals. ", "findings": "The ARIMAX (1, 1, 1) model demonstrated strong predictive capability, explaining 78% of the variance in the risk index. Forecasts indicate a sustained downward trend in systemic risk, with a projected reduction of approximately 40% over the forecast horizon compared to peak historical levels. All exogenous coefficients were statistically significant at the 5% level. ", "conclusion": "The developed framework provides a validated, quantitative tool for proactive risk management in process-control systems. The forecasting model successfully captures underlying risk dynamics, enabling evidence-based intervention planning. ", "recommendations": "Adoption of the presented methodological framework by national regulatory bodies and industrial operators is recommended. Future work should integrate real-time sensor data to transition from periodic to continuous risk forecasting. ", "key words": "process safety, risk forecasting, time-series analysis, ARIMAX, infrastructure engineering, predictive maintenance", "contribution statement": "This paper presents a novel application of an ARIMAX forecasting model to quantify and project process-control system risk in
Uwase et al. (Sat,) studied this question.