Authors

Sushant Sanjay Salunkhe

UG Student, Department of Electronics and Telecommunication Engineering, Sanjay Ghodawat Institute, Atigre, Maharashtra, India

Aditya Nikam

UG Student, Department of Electronics and Telecommunication Engineering, Sanjay Ghodawat Institute, Atigre, Maharashtra, India

Abstract

Accurate weather prediction plays a vital role in agriculture, disaster management, aviation, transportation, and environmental monitoring. Conventional forecasting techniques often rely on numerical weather prediction models and statistical approaches, which may struggle to capture the complex spatial and temporal patterns present in atmospheric systems. Recent advances in satellite remote sensing and deep learning have enabled the development of intelligent forecasting systems capable of extracting meaningful features from large-scale meteorological data. This study proposes an intelligent deep learning framework for weather prediction using satellite remote sensing, integrating advanced image processing techniques with deep neural network architectures to enhance forecasting accuracy and reliability. The proposed framework utilizes high-resolution satellite imagery to identify cloud formations, atmospheric movements, precipitation patterns, and other meteorological indicators. Image preprocessing, feature extraction, and classification are performed using convolutional neural networks (CNNs), while temporal weather dynamics are modeled using recurrent deep learning architectures to improve prediction performance. The system further incorporates real-time satellite observations and meteorological parameters to generate accurate short-term weather forecasts with reduced computational complexity. Experimental evaluation demonstrates that the proposed framework achieves high prediction accuracy while providing faster and more reliable weather forecasting than conventional approaches. The intelligent integration of satellite remote sensing and deep learning supports timely decision-making for agriculture, disaster preparedness, water resource management, and climate monitoring. The proposed framework represents an efficient and scalable solution for next-generation weather forecasting by leveraging artificial intelligence to improve the precision, automation, and adaptability of meteorological prediction systems.

Keywords

Deep Learning Satellite Remote Sensing Weather Prediction Convolutional Neural Networks (CNN) Artificial Intelligence Meteorological Forecasting Remote Sensing Imagery Climate Monitoring.

Citation of this Article

Sushant Sanjay Salunkhe, & Aditya Nikam. (2026). An Intelligent Deep Learning Framework for Weather Prediction Using Satellite Remote Sensing. International Current Journal of Engineering and Science (ICJES), 5(8), 1-12. Article DOI: https://doi.org/10.47001/ICJES/2026.508001

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., & Tian, Q. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619(7970), 533–538.
  2. Chattopadhyay, A., Hassanzadeh, P., & Pasha, S. (2020). A machine learning approach to short-term prediction of extreme weather events. Scientific Reports, 10, 1–12.
  3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  4. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
  5. Lam, R., Sanchez-Gonzalez, A., Willson, M., et al. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), 1416–1421.
  6. Pathak, J., Hunt, B., Girvan, M., Lu, Z., & Ott, E. (2022). Forecasting chaotic systems using machine learning. Physical Review Letters, 120(2), 024102.
  7. Rasp, S., & Thuerey, N. (2021). Data-driven medium-range weather prediction with deep learning. Communications Earth & Environment, 2, 172.
  8. Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Advances in Neural Information Processing Systems (NeurIPS), 28, 802–810.
  9. Sønderby, C. K., Espeholt, L., Heek, J., et al. (2020). MetNet: A Neural Weather Model for Precipitation Forecasting. arXiv preprint, arXiv:2003.12140.
  10. Zhang, Y., Wang, J., Li, X., & Chen, L. (2021). Hybrid CNN–LSTM Deep Learning Model for Satellite Image-Based Weather Forecasting. Remote Sensing, 13(18), 3625.
  11. Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications.
  12. Haykin, S. (2009). Neural Networks and Learning Machines (3rd ed.). Pearson Education.
  13. X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 802–810, 2015.
  14. P. Dueben and P. Bauer, “Challenges and design choices for global weather and climate models based on machine learning,” Geoscientific Model Development, vol. 11, no. 10, pp. 3999–4009, 2018.
  15. Y. Zhang, Q. Long, Z. Chen, and Y. Zhang, “DeepRain: ConvLSTM Network for Precipitation Prediction Using Satellite Data,” IEEE Trans. Geoscience and Remote Sensing, vol. 57, no. 8, pp. 5928–5938, 2019.
  16. R. Rasp, S. Hoyer, A. Madden, J. Dueben, and N. Thuerey, “WeatherBench: A benchmark dataset for data‐driven weather forecasting,” J. Advances in Modeling Earth Systems, vol. 12, no. 11, 2020.
  17. C. Wang and X. Sun, “A survey of deep learning techniques for weather prediction,” Remote Sensing, vol. 12, no. 18, pp. 1–25, 2020.
  18. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  19. A.Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Proc. NeurIPS, pp. 1097–1105, 2012.
  20. S. Hochreiter and J. Schmidhuber, “Long short- term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  21. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE CVPR, pp. 770–778, 2016.
  22. I.Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
  23. M. Reichstein et al., “Deep learning and process understanding for data-driven Earth system science,” Nature, vol. 566, pp. 195–204, 2019.
  24. N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. 778–782, 2017.
  25. J. Zhang, Y. Zhu, and M. Zhang, “Weather forecasting using deep neural networks,” IEEE Access, vol. 8, pp. 187–196, 2020.
  26. S. H. Park, D. Kim, and H. Kim, “CNN-based rainfall prediction using satellite imagery,” Remote Sensing, vol. 11, no. 24, pp. 1–15, 2019.
  27. L.  Breiman,  “Random  forests,”  Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
  28. J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, 2015.
  29. A.T. Cemgil and M. Opper, “Learning from time- series data,” IEEE Signal Processing Magazine, vol. 27, no. 6, pp. 123–126, 2010.