Weather Classification using Fusion Of Convolutional Neural Networks and Traditional Classification Methods | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Article 10, Volume 22, Issue 2, May 2022, Pages 84-96 PDF (781.7 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2022.117060.1156 | ||
| Authors | ||
| Moshira S. Ghaleb* 1; Hala Moushier2; Howaida Shedeed3; Mohamed Tolba4 | ||
| 1Scientic Computing , Faculty of Computer and Information Science Ain shams University , Cairo , Egypt | ||
| 2Fcis - Ain shams univ. | ||
| 3FCIS Ain shams univ. | ||
| 4Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt | ||
| Abstract | ||
| Abstract— The weather phenomenon is very important in routine lives. The weather prediction, road electronic monitoring, traffic communication, capping inversion (CAP), afforestation, and the adjustment of the environmental issues are important factors to many decisions. Weather images classification may help in decision support systems. There are traditional and intelligent ways that can sufficiently achieve weather image classification. Traditional methods enhance the classification accuracy and the usability of weather phenomena. Researchers approve that machine learning has achieved better accuracies based on deep learning neural networks. This paper compares three different intelligent models by using a weather image dataset. The first model uses a convolution neural network (CNN) to classify five categories of weather images. The second model uses a fusion of convolution neural network and Decision Tree (DT). The third one uses a fusion of CNN and Support Vector Machine (SVM). The three models are applied to the collected dataset from Github and Kaggle. The study has achieved 92%, 93%, and 94% for CNN, CNN+DT, and CNN+SVM respectively. The Proposed methods have achieved high recognition accuracies for weather forecasting. | ||
| Keywords | ||
| Deep Learning; Convolution neural network; Support Vector Machine; Decision Tree; Weather forecasting | ||
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