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

Amit Solanki; Dr. Sheshang Degadwala

doi:10.32628/CSEIT24103121

Authors

Amit Solanki Reserach Scholar, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author
Dr. Sheshang Degadwala Professor / HOD, Department of Computer Engineering, Sigma University, Vadodara, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT24103121

Keywords:

Weather Forecasting, Nowcasting, Next-Frame Prediction, Deep Learning, Computer Vision

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.

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References

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S. Patel, H. Patel, D. Vyas, and S. Degadwala, “Multi-Classifier Analysis of Leukemia Gene Expression from Curated Microarray Database (CuMiDa),” in Proceedings - 2nd International Conference on Smart Electronics and Communication, ICOSEC 2021, 2021, pp. 1174–1178. doi: 10.1109/ICOSEC51865.2021.9591854. DOI: https://doi.org/10.1109/ICOSEC51865.2021.9591854

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S. Degadwala, U. Chakraborty, P. Kuri, H. Biswas, A. N. Ali, and D. Vyas, “Real-Time Panorama and Image Stitching with Surf-Sift Features,” in Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, 2021, pp. 1111–1115. doi: 10.1109/ICICT50816.2021.9358586. DOI: https://doi.org/10.1109/ICICT50816.2021.9358586

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S. Degadwala, S. A. Musa, D. Vyas, and P. Mitra, “IoT Defence: An Internet Based Remote Area Monitoring and Control System,” in Proceedings of the 5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021, 2021, pp. 487–491. doi: 10.1109/ICECA52323.2021.9676144. DOI: https://doi.org/10.1109/ICECA52323.2021.9676144

S. Degadwala, B. Patel, and D. Vyas, “A Review on Indian State/City Covid-19 Cases Outbreak Forecast utilizing Machine Learning Models,” in Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, 2021, pp. 1001–1005. doi: 10.1109/ICICT50816.2021.9358506. DOI: https://doi.org/10.1109/ICICT50816.2021.9358506

S. Degadwala, D. Vyas, H. Dave, and A. Mahajan, “Visual Social Distance Alert System Using Computer Vision Deep Learning,” in Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020, 2020, pp. 1512–1516. doi: 10.1109/ICECA49313.2020.9297510. DOI: https://doi.org/10.1109/ICECA49313.2020.9297510

S. Degadwala, U. Chakraborty, S. Saha, H. Biswas, and D. Vyas, “EPNet: Efficient patch-based deep network for real-time semantic segmentation,” in Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 2020, pp. 611–615. doi: 10.1109/ICISS49785.2020.9316079. DOI: https://doi.org/10.1109/ICISS49785.2020.9316079