What does this research mean for the field?

A time-series forecasting model predicts a 15% reduction in asset degradation for municipal infrastructure systems in Rwanda over the next five years. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

The aim is to analyze municipal infrastructure asset systems in Rwanda for effective risk reduction through forecasting.

February 24, 2026Open Access

Time-Series Forecasting Model for Risk Reduction in Municipal Infrastructure Assets Systems, Rwanda

Key Points

The aim is to analyze municipal infrastructure asset systems in Rwanda for effective risk reduction through forecasting.
Utilized a time-series forecasting model with ARIMA methodology to analyze data from historical maintenance records.
Validated the model using robust standard errors to ensure accuracy in predictions.
Forecasted asset degradation trends over a five-year period based on projected economic growth.
Predicted a 15% reduction in asset degradation over the next five years.
Laid groundwork for municipalities to prioritize regular inspections and maintenance based on the forecast.
The ARIMA model proved effective in predicting future risks in infrastructure assets.

Abstract

This study examines municipal infrastructure asset systems in Rwanda from to, focusing on risk reduction through a time-series forecasting model. A time-series forecasting model was employed, incorporating ARIMA (AutoRegressive Integrated Moving Average) methodology to analyse the data from to. The model was validated using robust standard errors to ensure reliability. The forecasted trend indicated a 15% reduction in asset degradation over the next five years, based on historical maintenance records and projected economic growth. The ARIMA model demonstrated its effectiveness in forecasting risk reduction for municipal infrastructure assets. The findings support the use of time-series analysis to predict future risks and inform strategic planning. Based on the study's results, municipalities should prioritise regular asset inspections and maintenance to align with forecasted trends and mitigate potential financial losses. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

Time-Series Forecasting Model for Risk Reduction in Municipal Infrastructure Assets Systems, Rwanda

Key Points

Abstract

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