Forecasting Egypt’s Death Rate for Sustainable Development Planning Using Artificial Intelligence

In many developing countries export earnings account for a large percentage of this paper provides a comparative examination of two forecasting models, namely the Autoregressive Integrated Moving Average (ARIMA) and the Nonlinear Autoregressive (NAR), applied to predict the Death rate in Egypt from 2000 to 2020. The objective is to determine the most efficient model for demographic forecasting to assist in sustainable development planning. This study evaluates the predicted accuracy of traditional and AI-based approaches by conducting a thorough analysis and using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as benchmarks. The ARIMA model demonstrated superior performance in terms of RMSE, suggesting a closer fit to the actual data points, while the NAR model excelled in MAPE, indicating greater accuracy in percentage terms. These findings highlight the importance of model selection based on the specific application and objectives, particularly in the context of achieving Sustainable Development Goals (SDGs). This research not only contributes to the academic discourse on demographic forecasting but also offers practical insights for policymakers and planners in Egypt and similar contexts, underscoring the potential of advanced forecasting techniques in guiding sustainable development strategies.