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A Review on Rainfall Nowcasting Methodology using different ML and DL Models

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  • Amit Solanki
  • Sheshang Degadwala

Abstract

Weather is influenced by several factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth's rotation. Forecasting weather with high geographic precision is both challenging and computationally demanding. This study explores a nowcasting method for meteorological radar images. Building on the concept of an unsupervised representation problem in deep learning, next-frame prediction is an emerging area in computer vision where future images are predicted based on previous ones. This has numerous applications, including in robotics and autonomous driving. The paper reviews the latest next-frame prediction networks, categorizing them into two main approaches: machine learning and deep learning. It compares these strategies by discussing their respective advantages and disadvantages. Lastly, the paper outlines the most promising directions for future research.

Suggested Citation

  • Amit Solanki & Sheshang Degadwala, 2024. "A Review on Rainfall Nowcasting Methodology using different ML and DL Models," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(3), pages 461-472, June.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:216
    DOI: 10.32628/CSEIT24103121
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24103121
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