Performance Evaluation of Precision Poverty Alleviation with the Assistance of Wireless Communication Technology for Rural Revitalization

作者全名："Li, Yan; Liu, Zhiyuan"

作者地址："[Li, Yan] Chongqing Med Univ, Sch Marxism, Chongqing 400016, Peoples R China; [Liu, Zhiyuan] Chongqing Med Univ, Affiliated Hosp 3, Chongqing 400016, Peoples R China"

通信作者："Liu, ZY (通讯作者)，Chongqing Med Univ, Affiliated Hosp 3, Chongqing 400016, Peoples R China."

来源：WIRELESS COMMUNICATIONS & MOBILE COMPUTING

ESI学科分类：COMPUTER SCIENCE

WOS号：WOS:000852895800008

JCR分区：Q3

影响因子：2.146

年份：2022

卷号：2022

期号：　

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结束页：　

文献类型：Article

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摘要："In the context of rural revitalization, it is imperative to evaluate the performance of poverty alleviation scientifically, which can not only promote the removal of poverty areas and their sustainable development but also comprehensively evaluate the achievements of targeted poverty alleviation work. In this work, the rough set and Support Vector Machine- (SVM-) related concepts are first introduced to establish the required index system. Long-Range Radio (LoRa) wireless communication technology is adopted to collect relevant data, rough set is utilized to preprocess the data, and the importance and relative weight of each indicator are calculated. After elimination of redundant indexes, a new decision table is established, and a prediction model of SVM is established. In parameter optimization, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and grid search are employed. Experimental data show that the minimum error of GA is 1.82%, the test error is 5.11%, and the training error is 3.18%. The minimum error of PSO is 1.86%, the test error is 5.62%, and the training error is 3.15%. The minimum error of grid search method is 2.11%, the test error is 10.73%, and the training error is 2.34%. These three algorithms can optimize SVM parameters and effectively improve the performance evaluation results of targeted poverty alleviation by the model. By comparing the effect of the SVM model without rough set, it is found that the prediction accuracy of model can be improved by using rough set. Given the cross-validation error rate of the model and the predicted mean square error, GA is better than others. The established model makes up for the situation of strong subjectivity and low universal applicability in typical performance evaluation, enriches performance evaluation methods of targeted poverty alleviation, and is of certain practical reference value in performance evaluation of targeted poverty alleviation."

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