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Cloud type classification using deep learning with cloud images.

Mehmet Guzel1, Muruvvet Kalkan1, Erkan Bostanci1

  • 1Department of Computer Engineering, Ankara University, Ankara, Turkey.

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|January 10, 2024
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Summary
This summary is machine-generated.

This study uses deep learning and image processing to classify cloud types from images, achieving 97.66% accuracy with the Xception model. This advancement enhances weather forecasting and preparedness for hazardous conditions.

Keywords:
CNNCloud typesDeep learningImage classificationTransfer learning

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Area of Science:

  • Meteorology and atmospheric science.
  • Computer science, specifically artificial intelligence and machine learning.

Background:

  • Clouds are critical indicators of weather patterns, influencing daily life and providing warnings for severe conditions.
  • Accurate cloud classification is essential for improving weather forecasting and enabling proactive measures against hazardous weather events.

Purpose of the Study:

  • To develop an automated system for classifying cloud formations based on visual characteristics like shape and color.
  • To enhance the accuracy and reliability of weather prediction through advanced cloud analysis.

Main Methods:

  • Utilized image processing and deep learning techniques for cloud image classification.
  • Evaluated multiple deep learning models including MobileNet V2, Inception V3, EfficientNetV2L, VGG-16, Xception, ConvNeXtSmall, and ResNet-152 V2.
  • The Xception model was identified as the most effective for this task.

Main Results:

  • The Xception model achieved a high classification accuracy of 97.66%.
  • Demonstrated the potential of artificial intelligence in accurately detecting and classifying diverse cloud types.

Conclusions:

  • Integrating AI-powered cloud classification into weather forecasting systems can significantly improve prediction accuracy.
  • This research offers a novel approach to cloud study, enhancing weather preparedness and forecast reliability.