Using Deep learning to Improve the Classification of Weather Phenomena from Images | ||||
| آفاق آسيوية | ||||
| Volume 7, Issue 11, March 2023 PDF (996.36 K) | ||||
| Document Type: الدراسات | ||||
| DOI: 10.21608/sis.2023.206017.1080 | ||||
 View on SCiNiTO | ||||
| Author | ||||
abdelmonaim fakhry mohamad  
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| maadi mokatam - 7426 el hadaba maadi | ||||
| Abstract | ||||
| One of the most important phenomena at the moment is the identification of weather phenomena, which are crucial for numerous facets of daily life and are especially important for weather forecasts, transportation, tracking road conditions, farming, and forest management to protect of environment. Contrarily, few studies have attempted to classify images of actual weather events because it is challenging to do so using pictures, which are primarily reliant on human visual observations. This lessens the possibility of changing weather patterns. As far as we can determine, it takes time and is difficult to accurately distinguish between various weather events using traditional artificial vision. Even though some research has increased precision, also efficacy to recognizing phenomena employing artificial intelligence and even though AI approaches are better suited for categorization, they have discovered less different weather phenomena. This study suggests five artificial intelligence classification methods for meteorological events. In the meantime, we created a brand- The Weather Phenomenon Database (WEAPD), a brand-new dataset with 6,691 photos and 11 different types of weather phenomena, | ||||
| Keywords | ||||
| Deep; learning; Improve; Classification | ||||
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