Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model
Abstract Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing’s migrant workers, a new grey prediction model is constructed. The new model is constructed by intr...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88085-2 |
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| author | Dai Liu Chengxiang He Jianlong Ma |
| author_facet | Dai Liu Chengxiang He Jianlong Ma |
| author_sort | Dai Liu |
| collection | DOAJ |
| description | Abstract Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing’s migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%. |
| format | Article |
| id | doaj-art-30738116189d4b37a4f3e81fa5a96ac1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-30738116189d4b37a4f3e81fa5a96ac12025-02-02T12:24:17ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-88085-2Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst modelDai Liu0Chengxiang He1Jianlong Ma2Institute of Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business UniversitySchool of Logistics Engineering, Chongqing Finance and Economics CollegeSchool of International Business, Chongqing Finance and Economics CollegeAbstract Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing’s migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%.https://doi.org/10.1038/s41598-025-88085-2Rural migrant workers predictionGrey prediction theoryGrey Verhulst modelAccumulating order optimization |
| spellingShingle | Dai Liu Chengxiang He Jianlong Ma Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model Scientific Reports Rural migrant workers prediction Grey prediction theory Grey Verhulst model Accumulating order optimization |
| title | Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model |
| title_full | Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model |
| title_fullStr | Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model |
| title_full_unstemmed | Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model |
| title_short | Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model |
| title_sort | forecasting the number of rural migrant workers in chongqing using the optimized grey n verhulst model |
| topic | Rural migrant workers prediction Grey prediction theory Grey Verhulst model Accumulating order optimization |
| url | https://doi.org/10.1038/s41598-025-88085-2 |
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