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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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2021-01-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.004 |
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| Summary: | 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. |
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| ISSN: | 1001-9235 |