Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River
The spatial interpolation based on the limited observation data of precipitation stations is an effective means to explore the spatial characteristics of precipitation.This paper briefly introduces a spatial interpolation model based on the information diffusion theory,proposes an optimal informatio...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2021-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.004 |
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| author | HUANG Huaping YIN Kaixia JIN Gaoyang |
| author_facet | HUANG Huaping YIN Kaixia JIN Gaoyang |
| author_sort | HUANG Huaping |
| collection | DOAJ |
| description | The spatial interpolation based on the limited observation data of precipitation stations is an effective means to explore the spatial characteristics of precipitation.This paper briefly introduces a spatial interpolation model based on the information diffusion theory,proposes an optimal information diffusion interpolation model based on particle swarm optimization (PSO) and cross-validation (CV) with consideration of the disadvantages on key parameter estimation of existing experience information diffusion interpolation model,and evaluates the interpolation effect of the information diffusion interpolation model in terms of four time scales of year,quarter,month and day with inverse distance weighting,ordinary kriging method,universal kriging method and co-kriging method considering the elevation as comparison,taking the source regions of the Yellow River as an example.The results show that overall,the optimal information diffusion interpolation model has the highest accuracy,while the inverse distance weighting has the lowest accuracy.The accuracy of experience information diffusion interpolation model does not differ much from the other three spatial interpolation methods,and the accuracy differences between different methods decrease as the time scale decreases. |
| format | Article |
| id | doaj-art-73d34f5f8266428a8534258e1559f8b7 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-73d34f5f8266428a8534258e1559f8b72025-01-15T02:28:41ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352021-01-014247647012Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow RiverHUANG HuapingYIN KaixiaJIN GaoyangThe spatial interpolation based on the limited observation data of precipitation stations is an effective means to explore the spatial characteristics of precipitation.This paper briefly introduces a spatial interpolation model based on the information diffusion theory,proposes an optimal information diffusion interpolation model based on particle swarm optimization (PSO) and cross-validation (CV) with consideration of the disadvantages on key parameter estimation of existing experience information diffusion interpolation model,and evaluates the interpolation effect of the information diffusion interpolation model in terms of four time scales of year,quarter,month and day with inverse distance weighting,ordinary kriging method,universal kriging method and co-kriging method considering the elevation as comparison,taking the source regions of the Yellow River as an example.The results show that overall,the optimal information diffusion interpolation model has the highest accuracy,while the inverse distance weighting has the lowest accuracy.The accuracy of experience information diffusion interpolation model does not differ much from the other three spatial interpolation methods,and the accuracy differences between different methods decrease as the time scale decreases.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.004spatial characteristic of precipitationspatial interpolationcross validationparticle swarm optimizationinformation diffusionsource regions of the Yellow River |
| spellingShingle | HUANG Huaping YIN Kaixia JIN Gaoyang Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River Renmin Zhujiang spatial characteristic of precipitation spatial interpolation cross validation particle swarm optimization information diffusion source regions of the Yellow River |
| title | Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River |
| title_full | Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River |
| title_fullStr | Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River |
| title_full_unstemmed | Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River |
| title_short | Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River |
| title_sort | information diffusion interpolation model based on the pso cv algorithm and its application on source regions of the yellow river |
| topic | spatial characteristic of precipitation spatial interpolation cross validation particle swarm optimization information diffusion source regions of the Yellow River |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.004 |
| work_keys_str_mv | AT huanghuaping informationdiffusioninterpolationmodelbasedonthepsocvalgorithmanditsapplicationonsourceregionsoftheyellowriver AT yinkaixia informationdiffusioninterpolationmodelbasedonthepsocvalgorithmanditsapplicationonsourceregionsoftheyellowriver AT jingaoyang informationdiffusioninterpolationmodelbasedonthepsocvalgorithmanditsapplicationonsourceregionsoftheyellowriver |