What question did this study set out to answer?

The study aims to replicate and evaluate the forecasting model for risk reduction in water treatment systems.

March 13, 2026Open Access

Replication and Validation of a Time-Series Forecasting Model for Risk Reduction Diagnostics in Senegalese Water Treatment Systems (2000–2026)

Puntos clave

The study aims to replicate and evaluate the forecasting model for risk reduction in water treatment systems.
Conducted a replication study using longitudinal operational data from multiple water treatment plants.
Re-implemented the ARIMA forecasting model.
Assessed forecast performance using mean absolute scaled error and 95% prediction intervals.
Confirmed the model's directional trend forecasting capability.
Identified an 18% overestimation of risk reduction magnitude compared to observed outcomes.
Found narrower prediction intervals than expected, indicating underestimated uncertainty.

Resumen

"background": "Time-series forecasting models are critical for proactive infrastructure management, yet their performance in real-world, resource-constrained settings requires rigorous validation. Original research proposed a model for diagnostic risk reduction in water treatment systems, but its generalisability to specific regional operational contexts remains untested. ", "purpose and objectives": "This study aimed to independently replicate and critically evaluate the forecasting model's methodological robustness and predictive accuracy when applied to operational data from water treatment facilities. The objective was to validate its utility as a diagnostic tool for engineering risk reduction. ", "methodology": "A replication study was conducted using a longitudinal operational dataset from multiple treatment plants. The core model, an autoregressive integrated moving average (ARIMA) formulation \ᵈ yt = c + =1^{p\ \ᵈ yt-i + =1^q\ -j + \, was re-implemented. Forecast performance was assessed via mean absolute scaled error (MASE) and 95% prediction intervals. ", "findings": "The replication confirmed the model's directional trend forecasting capability but revealed a systematic overestimation of risk reduction magnitude by approximately 18% compared to observed outcomes. Prediction intervals were found to be narrower than the empirical error distribution suggested, indicating underestimated uncertainty. ", "conclusion": "While structurally sound, the model requires parameter recalibration for the specific context to improve its precision as a diagnostic tool. The replication underscores the importance of contextual validation for engineering models. ", "recommendations": "Model deployment should incorporate locally derived parameter priors. Practitioners are advised to apply wider uncertainty bounds (approx. +22%) to forecasts when using the original specification for maintenance planning. ", "key words": "replication study, time-series forecasting, water treatment, risk reduction, model validation, infrastructure diagnostics", "contribution statement": "This study provides the first independent validation and methodological critique of the specified forecasting model, offering a

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