Atmospheric visibility prediction based on multi-model fusion

Shiyang Yan; Yu Zheng; Yixuan Chen; Baoren Li

doi:10.1117/12.2611922

11 November 2021 Atmospheric visibility prediction based on multi-model fusion

Shiyang Yan, Yu Zheng, Yixuan Chen, Baoren Li

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760R (2021) https://doi.org/10.1117/12.2611922
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

In this paper, a combinatorial algorithm-based visibility prediction method is proposed for improving the accuracy of visibility prediction. Firstly, four algorithms, namely support vector machine, kernel extreme learning machine, random forest and RBF neural network, are used as the basis functions for prediction, then the objective function of the combined prediction is constructed, the cuckoo search is used to optimise the calculation of the weighting coefficients of the combined prediction, and finally the combined prediction results are obtained. The experimental results show that the combined prediction algorithm proposed in this paper can effectively improve the accuracy of visibility prediction, and has certain application and research value.

Citation Download Citation

Shiyang Yan, Yu Zheng, Yixuan Chen, and Baoren Li "Atmospheric visibility prediction based on multi-model fusion", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760R (11 November 2021); https://doi.org/10.1117/12.2611922

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