Methodological Evaluation of Regional Monitoring Networks for Risk Reduction in Tanzania Using Time-Series Forecasting Models

This study focuses on evaluating regional monitoring networks in Tanzania for risk reduction, particularly through the application of time-series forecasting models. A systematic approach was employed, including data collection from existing monitoring networks, model development using ARIMA (AutoRegressive Integrated Moving Average) models, and validation through cross-validation methods. Uncertainty in forecasts was quantified with confidence intervals around the predicted values. The analysis revealed a significant reduction in forecasted risk levels by approximately 30% when integrating regional monitoring data into time-series forecasting models compared to baseline predictions without such integration. This study confirms the effectiveness of using ARIMA models for enhancing risk assessments through integrated monitoring networks, offering substantial improvement over traditional methods. The findings suggest that policymakers should prioritise investment in comprehensive and interconnected regional monitoring systems as a key strategy for mitigating environmental risks. Tanzania, Monitoring Networks, Risk Reduction, Time-Series Forecasting, ARIMA Models Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.