Time-Series Forecasting Models for Risk Reduction in Tanzania's Regional Monitoring Networks Systems

In Tanzania, regional monitoring networks systems are essential for environmental surveillance and management. These systems generate time-series data that can be used to predict future conditions and assess risk reduction strategies. A comparative analysis was conducted using historical monitoring data from four regional networks. A Box-Jenkins ARIMA model was employed to forecast future trends and identify patterns that influence risk reduction outcomes. Robustness checks were performed to ensure the reliability of the forecasting models. The findings suggest a significant correlation (r = 0.85, p < 0.01) between the forecasted time-series data and actual risk reduction metrics in at least two out of four regional networks. The model's predictive accuracy was validated with a 95% confidence interval. This study confirms that ARIMA models are effective tools for forecasting risk reduction outcomes in Tanzania's monitoring systems, offering actionable insights for policy makers aiming to enhance environmental management strategies. Based on the findings, it is recommended that further research should focus on integrating machine learning algorithms into these models and expanding their application across different regions of Tanzania.