using a deep neural network model to forecast the population dynamics in iran

Iran has undergone unique demographic changes in the recent decades. this paper aims to project the natural population growth rate -npg over the next decade (2024–2034), which would offer a comprehensive perspective into the future of iran's population dynamics. in this regard, to accomplish the above task, this work deals with the projection of most important demographic measures that characterize the population process, namely the crude birth rate -cbr, the crude death rate -cdr, and the population doubling time- pdt. to this end, a deep neural network modeling approach was developed and applied. forecasting with deep neural networks is one of the most important and influential techniques used in machine learning and artificial intelligence. the data-driven model, based on data obtained from the statistical center of iran, was subsequently implemented for model development in matlab.results from the paper indicate that the cbr drops from 11.3 per thousand in 2024 to 9.3 per thousand in 2034. on the other hand, the cdr increases from 5.2 per thousand in 2025 to 6.1 per thousand in 2034. with this effect, the npg decreases from 6.1 per thousand in 2025 to 3.2 per thousand in 2034. lastly, pdt for the population is forecasted to rise from 114 years in 2025 to 218 years in 2034.this study presents a deep neural network model for describing and forecasting the complex dynamics of population changes in iran. constructing this model helps policy-makers and planners use the forecasted population dynamics to design and implement programs and policies with greater precision. iran has undergone unique demographic changes in the recent decades. this paper aims to project the natural population growth rate -npg over the next decade (2024–2034), which would offer a comprehensive perspective into the future of iran's population dynamics. in this regard, to accomplish the above task, this work deals with the projection of most important demographic measures that characterize the population process, namely the crude birth rate -cbr, the crude death rate -cdr, and the population doubling time- pdt. to this end, a deep neural network modeling approach was developed and applied. forecasting with deep neural networks is one of the most important and influential techniques used in machine learning and artificial intelligence. the data-driven model, based on data obtained from the statistical center of iran, was subsequently implemented for model development in matlab.results from the paper indicate that the cbr drops from 11.3 per thousand in 2024 to 9.3 per thousand in 2034. on the other hand, the cdr increases from 5.2 per thousand in 2025 to 6.1 per thousand in 2034. with this effect, the npg decreases from 6.1 per thousand in 2025 to 3.2 per thousand in 2034. lastly, pdt for the population is forecasted to rise from 114 years in 2025 to 218 years in 2034.this study presents a deep neural network model for describing and forecasting the complex dynamics of population changes in iran. constructing this model helps policy-makers and planners use the forecasted population dynamics to design and implement programs and policies with greater precision.