Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization
Considering that the global navigation satellite system (GNSS) has the influence of positioning and atmospheric signals from time to time in meteorology, errors caused by moisture, and so on in the effect of the propagation path, these factors have led to the influence of various indexes of meteorol...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6415589 |
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| _version_ | 1832561421822459904 |
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| author | Li Yang Meng Zhang Yunhan Zhang |
| author_facet | Li Yang Meng Zhang Yunhan Zhang |
| author_sort | Li Yang |
| collection | DOAJ |
| description | Considering that the global navigation satellite system (GNSS) has the influence of positioning and atmospheric signals from time to time in meteorology, errors caused by moisture, and so on in the effect of the propagation path, these factors have led to the influence of various indexes of meteorological factors. In this study, a meteorological prediction algorithm based on the CNSS and particle swarm optimization is proposed. Aiming at the phenomenon that the particle swarm optimization (PSO) algorithm is prone to slow convergence speed and low optimization accuracy and there is a local optimal but cannot achieve the global optimal, an adaptive Kent chaotic map PSO algorithm is proposed. Through the comprehensive analysis of the meteorological input indicators in the GNSS, a noncurrent weight evaluation system is proposed. Under different evaluation systems, the PSO algorithm is applied, and PCA weight can obtain the best prediction effect. Then, the GA model, PSO model, and ADPSO model are used to predict PM2.5 index in meteorology. The results show that the proposed ADPSO algorithm has a good performance in RMSE, MAE, and R2 model evaluation. |
| format | Article |
| id | doaj-art-46866f0042b144a38800efdec1e2d1b1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-46866f0042b144a38800efdec1e2d1b12025-02-03T01:25:01ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/64155896415589Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm OptimizationLi Yang0Meng Zhang1Yunhan Zhang2College of Environment and Planning, Henan University, Kaifeng 475000, Henan, ChinaCollege of Environment and Planning, Henan University, Kaifeng 475000, Henan, ChinaCollege of Environment and Planning, Henan University, Kaifeng 475000, Henan, ChinaConsidering that the global navigation satellite system (GNSS) has the influence of positioning and atmospheric signals from time to time in meteorology, errors caused by moisture, and so on in the effect of the propagation path, these factors have led to the influence of various indexes of meteorological factors. In this study, a meteorological prediction algorithm based on the CNSS and particle swarm optimization is proposed. Aiming at the phenomenon that the particle swarm optimization (PSO) algorithm is prone to slow convergence speed and low optimization accuracy and there is a local optimal but cannot achieve the global optimal, an adaptive Kent chaotic map PSO algorithm is proposed. Through the comprehensive analysis of the meteorological input indicators in the GNSS, a noncurrent weight evaluation system is proposed. Under different evaluation systems, the PSO algorithm is applied, and PCA weight can obtain the best prediction effect. Then, the GA model, PSO model, and ADPSO model are used to predict PM2.5 index in meteorology. The results show that the proposed ADPSO algorithm has a good performance in RMSE, MAE, and R2 model evaluation.http://dx.doi.org/10.1155/2021/6415589 |
| spellingShingle | Li Yang Meng Zhang Yunhan Zhang Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization Complexity |
| title | Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization |
| title_full | Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization |
| title_fullStr | Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization |
| title_full_unstemmed | Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization |
| title_short | Research on the Meteorological Prediction Algorithm Based on the CNSS and Particle Swarm Optimization |
| title_sort | research on the meteorological prediction algorithm based on the cnss and particle swarm optimization |
| url | http://dx.doi.org/10.1155/2021/6415589 |
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