Research On Early Warning Evaluation Of Poverty Reduction In The Background Of Precision Poverty Alleviation In Hebei Province

The research on the problem of returning to poverty is of great significance to consolidating the great results of poverty alleviation in China.However,most of the research fields on the problem of returning to poverty are qualitative studies,mostly the research on the causes of returning to poverty and the coping strategies after the return Few studies have been conducted to prevent the phenomenon of returning to poverty,and it is difficult to control the occurrence of returning to poverty from the root cause.With the advancement of the national poverty alleviation strategy,to ensure that all the rural poor are lifted out of poverty by 2020,we must find the root cause of the rural poor out of poverty,establish a sound early warning mechanism for poverty alleviation,and link the county,township,and village levels.A stumbling block in China's comprehensive poverty alleviation.Based on the data of 301 families in poverty-stricken areas in Hebei Province,combined with the theory of early warning of return to poverty,a quantitative study was made on the issue of return to poverty using statistical methods.Based on the survey and analysis of the causes of returning to poverty in Hebei Province,the early warning evaluation of returning to poverty was divided into four parts: viability early warning evaluation,development ability early warning evaluation,risk response ability early warning evaluation and emergency ability early warning evaluation.Early warning evaluation indicators,using AHP to determine indicator weights,and BP neural network constructed an empirical model of early poverty evaluation in Hebei Province.The main conclusions drawn from the research are as follows:(1)The causes of poverty return in poor areas of Hebei Province are obtained through investigation and analysis,which are mainly divided into four categories: inadequate viability,inadequate development ability,inadequate risk response ability,and insufficient emergency response ability.(2)Four types of early-warning evaluation indicators were selected based on the principles of science,dynamics,and quantifiability.(3)An analytic hierarchy process and BP neural network method were used to construct an early warning evaluation model for back to poverty,and the validity of the model was verified using MATLAB software.(4)Proposed corresponding early-warning countermeasures for different early-warning warning levels,which provides a reference for effectively preventing return to poverty and achieving sustainable poverty alleviation.