Greenhouse gas prediction method based on particle swarm optimized SVR

Xufeng Wang; Mingxue Bi

doi:10.1117/12.2660063

3 February 2023 Greenhouse gas prediction method based on particle swarm optimized SVR

Xufeng Wang, Mingxue Bi

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251126 (2023) https://doi.org/10.1117/12.2660063
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

To solve the problems of low accuracy of greenhouse gas detection and difficult selection of model parameters, this paper proposes a Support Vector Machine Regression (SVR), algorithm based on Particle Swarm Optimization (PSO). By comparing the performance of four common kernel functions of SVR on the test set, the kernel function with the best performance is selected as the kernel function of SVR. On this basis, comparing the Grid Search method (GridSearchCV) with the PSO, the optimal combination of super parameters C and gamma are selected. The results show that the PSO can efficiently select the optimal combination of super parameters and greatly improve the modeling efficiency. Finally, the greenhouse gas concentration of the SVR optimized based on the two algorithms is estimated through experiments. The accuracy of the optimized SVR algorithm reaches 94.42%, which improves the model’s prediction accuracy.

Citation Download Citation

Xufeng Wang and Mingxue Bi "Greenhouse gas prediction method based on particle swarm optimized SVR", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251126 (3 February 2023); https://doi.org/10.1117/12.2660063

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