State Prediction of Poverty Alleviation Objects Based on HMM and Multidimensional Data

Authors

Jun He, 1.College of Information Engineering, Kunming University, Kunming 650214, China;3.Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan, Kunming 650214, China;Follow
Sunyan Hong, 1.College of Information Engineering, Kunming University, Kunming 650214, China;3.Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan, Kunming 650214, China;Follow
Yifang Zhou, 2.Information Center, Kunming University, Kunming 650214, China;
Shikai Shen, 1.College of Information Engineering, Kunming University, Kunming 650214, China;
Muquan Zou, 1.College of Information Engineering, Kunming University, Kunming 650214, China;3.Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan, Kunming 650214, China;

Abstract

Abstract: In order to solve the problems of inaccurate prediction of poverty, poverty reduction and poverty returen, and the difficulty in identifying the key factors affecting the state transition, 8 key features and 22 observed states are extracted from the poverty reduction basic data and multi-industry data. The relationship between observed state and implied state is constructed, and the hidden markov model (HMM) of poverty alleviation is established. Data of a deep poverty county for three consecutive years are used as samples for parameter training, test experiment and result verification. The results show that the method has a strong prediction ability for back poverty, poverty and poverty alleviation with low error rate, and can accurately identify the key elements affecting poverty return. The method is of great practical significance for guiding the precise poverty alleviation work.

Recommended Citation

He, Jun; Hong, Sunyan; Zhou, Yifang; Shen, Shikai; and Zou, Muquan (2022) "State Prediction of Poverty Alleviation Objects Based on HMM and Multidimensional Data," Journal of System Simulation: Vol. 34: Iss. 5, Article 18.
DOI: 10.16182/j.issn1004731x.joss.20-1006
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss5/18

First Page

1118

Revised Date

2021-02-24

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-1006

Last Page

1126

CLC

TP311;TP391.9

Recommended Citation

Jun He, Sunyan Hong, Yifang Zhou, Shikai Shen, Muquan Zou. State Prediction of Poverty Alleviation Objects Based on HMM and Multidimensional Data[J]. Journal of System Simulation, 2022, 34(5): 1118-1126.

Corresponding Author

Sunyan Hong,hongsunyan@126.com

DOI

10.16182/j.issn1004731x.joss.20-1006