Weather classification using meta-based Random forest fusion of transfer learning models

Abstract

Weather classification into multiple categories is an essential task for many applications, including farming, military, transport, airlines, navigation, agriculture, etc. A few pieces of research give attention to this field and the current state-of-art methods have limitations, including low accuracy and limited weather conditions. In this study, a new weather classification meta-based fusion of the transfer deep learning model is introduced. The study takes into account all possible weather conditions and utilizes the fusion technique to improve the performance. First, the weather images are pre-processed and a data augmentation process is performed. These images are fed into five transfer deep learning models (XceptionNet, VGG16, ResNet50V2, InceptionV3, and DenseNet201). Then, the meta-based random forest fusion, the meta-based bagging fusion, and the score-level fusion are applied. Finally, all individual and fusion models are evaluated. Experiments were conducted on the WEAPD dataset which includes 11 categories. Results prove that the best performance is related to the meta-based ransom forest fusion method with 96% accuracy. The current study is also compared with the current state-of-art methods, and the comparison proves the robustness and high performance of the current study especially the fact that the current study achieves the best performance on the WEAPD dataset compared to studies worked on the same dataset. The current study proves that meta-based RF fusion is a promising methodology to address the weather classification problem. This outcome can be used by future study to improve the weather classification fusion and ensemble methodologies.