Analysis and prediction model of population contribution in underdeveloped areas based on GBDT-XGBoost algorithm

Tianjiao Liu; Yueyang Zhu; Cong Yao

doi:10.1117/12.3051955

18 November 2024 Analysis and prediction model of population contribution in underdeveloped areas based on GBDT-XGBoost algorithm

Tianjiao Liu, Yueyang Zhu, Cong Yao

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030F (2024) https://doi.org/10.1117/12.3051955
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

This paper takes Guizhou Province in China as an example to predict the future Development trend in underdeveloped regions combined with the influencing factors of Research sample mobility. Based on XGBoost (Extreme Gradient Lifting Tree) algorithm, several sample flow prediction models are constructed by quantifying the theme feature vectors as explanatory variables and taking the number of Research sample flow rate as explanatory variables. The results show that with the improvement of science and technology standards in China, in the five years 2021-2025, the gap between the less developed regions and the more developed regions is still large, in the state of net outflow, and the outflow number is increasing year by year.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianjiao Liu, Yueyang Zhu, and Cong Yao "Analysis and prediction model of population contribution in underdeveloped areas based on GBDT-XGBoost algorithm", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030F (18 November 2024); https://doi.org/10.1117/12.3051955

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