Small Town Population Forecasting and Planning Method Based on Genetic Algorithm and BP Neural Network

It is very important to accurately predict the population pattern in the framework of spatial planning in the township development track. In this paper, the basic principle and application field of population forecasting method of urban spatial planning are deeply studied, and the applicability of BP neural network method of genetic evolution to predict population size is described. The study initially used genetic algorithms to refine the initial weights and structure of BP neural networks to improve their proficiency and generalization ability in the interpretation of demographic data. The empirical results show that the method produces superior predictive performance on multiple township demographic data sets, especially when trying to cope with complex population dynamics. In addition, when benchmarked against traditional forecasting models, the technology showed significant enhancements in the accuracy, stability, and adaptability of predictive models. These results suggest that combining GA-driven evolution with BP neural networks provides a more robust and precise tool for population prediction.