Comparative Methodological Evaluation and Time-Series Forecasting for Risk Reduction in Senegalese Water Treatment Systems (2000–2026)

Water treatment infrastructure in many developing nations faces significant operational and maintenance challenges, leading to variable service quality and public health risks. A systematic, quantitative framework for assessing methodological approaches and forecasting future performance is required for proactive asset management. This study conducts a comparative evaluation of methodological approaches for assessing water treatment systems and develops a robust time-series forecasting model to quantify projected risk reduction from infrastructure interventions. A comparative analysis of assessment methodologies was performed using operational data from multiple facilities. A seasonal autoregressive integrated moving average (SARIMA) model, specified as (1 - B) (1 - B) ^dXₜ = (1 + B) ₜ, was developed and validated for forecasting critical water quality and operational parameters. Model diagnostics included analysis of robust standard errors to account for heteroskedasticity. The SARIMA model achieved a high forecasting accuracy, with a mean absolute percentage error below 8% for turbidity levels. The comparative analysis revealed that integrated performance-index methodologies outperformed conventional compliance-checking approaches by providing a 25% more sensitive indicator of incipient system failure. The integrated methodological framework, coupled with the forecasting model, provides a powerful evidence-based tool for engineers and policymakers to prioritise interventions and allocate resources efficiently for sustained risk reduction. Adoption of the integrated performance-index methodology for routine system assessments is recommended. Water authorities should implement the forecasting model for predictive maintenance scheduling and long-term strategic planning. water treatment, risk assessment, time-series analysis, forecasting, infrastructure management, SARIMA This paper presents a novel integrated framework that combines a comparative methodological evaluation with a validated forecasting model, providing a new engineering tool for quantifying future risk reduction in water infrastructure.