Work

Prediction and pattern recognition of the large-scale poverty-returning risks: empirical evidence from machine learning

Year: 2024

Type: article

Abstract: How to effectively predict the risk of large-scale poverty-returning and realize prior targeted intervention are essential measures to prevent large-scale poverty-returning. In view of this, based on the Sustainable Livelihoods Framework, this paper employs machine learning algorithms to study the prediction and pattern recognition of large-scale poverty-returning risks using the China Family Panel Studies (CFPS) and satellite remote sensing data. Our findings demonstrate a remarkable prediction accuracy rate of 91.23% for large-scale poverty-returning, employing the random forest algorithm. Notably, key contributing variables to this prediction encompass education, health, and government subsidies, highlighting the potential of supervised learning methods in predicting large-scale poverty-returning risk. Furthermore, the K-means clustering analysis reveals three discernible risk patterns within large-scale poverty-returning, including low-risk, three-risk, and five-risk patterns. The findings could provide scientific empirical evidence for the governance of different types of large-scale poverty-returning risks.

Source: The International Food and Agribusiness Management Review

DOI: 10.22434/ifamr.1096

Authors: Hanjie Wang, Wenpeng Huang, Zhijian Yu, Jiali Han

Institutions: Southwest University, University of Göttingen, Southwest University of Political Science & Law

Language: English

Topic: Income, Poverty, and Inequality

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: No poverty, Decent work and economic growth, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,078