Effectiveness evaluation of rural targeted poverty alleviation based on SSA-BPNN

doi:10.14088/j.cnki.issn0439-8114.2022.23.045

Abstract

Abstract: In order to achieve the effectiveness evaluation of targeted poverty alleviation, the rural targeted poverty alleviation effectiveness evaluation model based on the salp swarm algorithm （SSA） optimization back propagation neural network （BPNN） based on farmers’ satisfaction was established. Firstly, from the perspectives of living environment dimensions, living conditions, the effects of targeted poverty alleviation policies, and human development and social security, the indicator system for evaluating the effectiveness of targeted poverty alleviation based on farmers’ satisfaction was established. Secondly, the score data of 16 secondary indicators for the effectiveness evaluation of targeted poverty alleviation and the evaluation grade of the effectiveness of targeted poverty alleviation were used as the input vector and the output vector of BPNN to establish a BPNN model for the effectiveness evaluation of targeted poverty alleviation. Finally, SSA was used to optimize the initial weights and thresholds of the BPNN model, and establish the SSA-BPNN targeted poverty alleviation effectiveness evaluation model. The results showed that compared with other algorithms, SSA-BPNN had a higher accuracy rate and provided a method for evaluating the effectiveness of targeted poverty alleviation.

Key words: back propagation neural network（BPNN）, salp swarm algorithm（SSA）, targeted poverty alleviation, satisfaction, effectiveness evaluation

CLC Number:

F323.8

LAI A-long. Effectiveness evaluation of rural targeted poverty alleviation based on SSA-BPNN[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(23): 229-233.

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