# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
## 4 Conclusions and Discussion  
In this paper, we propose an image-based fog observation method, an improved Random Forest model integrated with the hierarchical and k-medoids clustering on the StyleGAN2-ADA data augmentation, which addresses the issue of dataset imbalance. Key fog-related features were studied. Performances of VGG16, VGG19, ResNet50, and DenseNet169 were compared and analyzed. The experiment shows that the improved RF approach has gained significantly increased observation accuracy and a decreased computational cost.  
(1) The StyleGAN2-ADA for data augmentation effectively mitigated the issue of dataset imbalance. Increasing the proportion of the dense fog grades, it reduces the risk of overfitting greatly, especially with a limited dataset. Additionally, data augmentation accelerated the model's training convergence speed by 30-50%.  
(2) Key fog-related features were identified through the feature aggregation test for fog density estimation. Five features are discarded, and 11 features that have a bigger correlation with fog density were selected such as MSCN variance, dark channel, and chroma, to name a few. These features describe the fog image characteristics best, which are capable of improving both computational efficiency and estimation performance.  
(3) To overcome the limitations of traditional Random Forest models in handling high-dimensional data and complex nonlinear relationships, hierarchical and k-medoid clustering is integrated with the RF model. A performance comparison with other deep learning models, the VGG16, the VGG19, the ResNet50, and the DenseNet169, is made both on the initial dataset and the augmented dataset.  
(4) When the initial dataset is used, the ResNet50 model performed the best in terms of average accuracy. The improved Random Forest model matches with the ResNet50 in terms of average accuracy but gains high performance in lighter fog conditions.  
(5) With data augmentation, the estimation accuracy of all models improved significantly. Notably, the improved Random Forest model achieved further enhancements in accuracy across all fog grades, reaching 89.8% in very dense fog and 91.7% in dense fog, with an average accuracy increase to 93.0%, which outperformed other deep learning models, with accuracy improvements ranging from 1.8% to 6.8%.  
It is noted that the experiment was very short and the image dataset was limited in space and time. Despite the model's very good performance, further validation is still needed in future work. Furthermore, the algorithm still needs optimization in practical applications and a more effective feature extraction method should be developed. The classified light fog is minted with the haze, which is similar in terms of visibility but is different in local humidity. In practice, according to the meteorological regulation, the haze can be distinguished from the fog whose relative humidity is over 80%. For light fog image-based estimation, humidity observation is needed for future work.  
[PERSON] took a lead role in data processing, article drafting, and proofreading. [PERSON] focused on data collection, providing the essential datasets for our analysis. [PERSON] was instrumental in providing the concept. [PERSON] took a role in project administration. All authors reviewed and commented on the original draft of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 41931075, 41961144015) and Observational Experiment Project of Meteorological Observation. Center of China Meteorological Administration (Grant No. GCSYJH24-21).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.