Visibility Analysis and Time Series Forecasting

Authors

  • Asoke Nath  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, India
  • Swapnil Saha  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, India
  • Himashri Basu  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, India
  • Shalini Torcato  Department of Computer Science, St. Xavier's College (Autonomous), Kolkata, India

DOI:

https://doi.org/10.32628/CSEIT2390321

Keywords:

Visibility Prediction, Time Series Forecasting, XGBoost, LSTM

Abstract

Visibility prediction and time series forecasting are two important fields in the domain of data analysis and machine learning. Visibility prediction deals with the estimation of atmospheric visibility or the distance over which objects can be clearly seen in a particular location, while time series forecasting involves predicting future values based on historical data. Both these fields have a wide range of applications, including transportation safety, air quality monitoring, and renewable energy management. However, the current prediction of visibility is mostly based on the numerical prediction method similar to the weather prediction [1]. In the present study the authors proposed a method using XGBoost(Extreme Gradient Boosting) and Unsupervised Models(ARIMA, ARMA, LSTM) models to build a weather visibility prediction system. The results obtained were very close to actual dataset.

References

  1. Chuang Zhang, Ming Wu, Jinyu Chen and Kaiyan Chen, “Weather Visibility Prediction Based on Multimodal Fusion”.Article in IEEE Access –June 2019
  2. Y. He, X. Sun, L. Gao, and B. Zhang, “Ship detection without sea-landsegmentation for large-scale high-resolution optical satellite images,” 07 2018, pp. 717–720.
  3. Z. W. Wang, C. L. Zhang, C. Su, and C. L. Cheng, “On modeling ofatmospheric visibility classification forecast with nonlinear support vectormachine,” in International Conference on Natural Computation, 2009.
  4. R. F. Chevalier, G. Hoogenboom, R. W. Mcclendon, and J. A. Paz,“Support vector regression with reduced training sets for air temperatureprediction: a comparison with artificial neural networks,” Neural Computing and Applications, vol. 20, no. 1, pp. 151–159, 2011.
  5. C. Marzban, S. Leyton, and B. Colman, “Ceiling and visibility forecastsvia neural networks,” Weather & Forecasting, vol. 22, no. 3, pp. 466–479,2006.
  6. Allan C. Just, Yang Liu, MeytarSorek-Hamer, Johnathan Rush, Michael Dorman, Robert Chatfield, Yujie Wang, Alexei Lyapustin, ItaiKloog, “Gradient Boosting Machine Learning to Improve Satellite-Derived Column Water Vapor Measurement Error”
  7. Jeongmook Park, Yongkyu Lee, and Jungsoo Lee, Researcher, Forest ICT Research Center, National Institute of Forest Science Department of Forest Management, Kangwon National University, Chuncheon, Republic of Korea-“ Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data”
  8. “Nonlinear analysis and visibility forecast of the relation between atmospheric visibility and pm2.5 concentration and relative humidity inwuhan,” Journal of Meteorology, vol. 74, no. 2, 2016.
  9. R. M. Chmielecki and A. E. Raftery, “Probabilistic visibility forecastingusing bayesian model averaging,” Monthly Weather Review, vol. 139,no. 5, pp. 1626–1636,2016.
  10. D. Bari, “Visibility prediction based on kilometric nwp model outputsusing machine learning regression,” in IEEE 14th International Conference on e Science, 2018.
  11. Z. Ma, J. H. Xue, A. Leijon, Z. H. Tan, Z. Yang, and J. Guo, “Decorrelationof neutral vector variables: Theory and applications,” IEEE Transactionson Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1–15,2016.
  12. W. Kai, Z. Hong, A. Liu, and Z. Bai, “The risk neural network basedvisibility forecast,” in International Conference on Natural Computation,2009.
  13. C. Marzban, S. Leyton, and B. Colman, “Ceiling and visibility forecastsvia neural networks,” Weather & Forecasting, vol. 22, no. 3, pp. 466–479,2006.
  14. K. Abhishek, M. Singh, S. Ghosh, and A. Anand, “Weather forecastingmodel using artificial neural network,” Procedia Technology, vol. 4,pp. 311 – 318, 2012, 2nd International Conference on Computer,Communication, Control and Information Technology( C3IT-2012) onFebruary 25 - 26, 2012. [Online].Available: http://www.sciencedirect.com/science/article/pii/S221201731200326X
  15. Y. T. Tsai, Y. R. Zeng, and Y. S. Chang, “Air pollution forecasting using rnn with lstm,” in 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology-Congress,2018.
  16. H. Tian, K. Li, and M. Bo, “A novel prediction modeling scheme based on multiple information fusion for day-ahead electricity price,” in Chinese Control and Decision-Conference,2011.
  17. H. Chen and S. Huang, “A comparative study on model selection and multiple model fusion,” in International Conference on Information Fusion,2005
  18. M. Sheng, K. Hu, G. Hao, and X. Wang, “Small fire smoke region location and recognition in satellite image,” in International Congress on Image & Signal Processing,2017.
  19. S. Miao, H. Lin, H. Gao, and L. Dong, “Strip smoke and cloud recognition in satellite image,” in International Congress on Image & Signal Processing, 2017.
  20. https://www. turing.com/kb/comprehensive-guide-to-lstm-rnn
  21. https://www.tableau.com/learn/articles/time-series-forecasting#:~:text=Time%20series%20forecasting%20occurs%20when,drive%20future%20strategic%20decision%2Dmaking
  22. https://www.edureka.co/blog/boosting-machine-learning

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

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How to Cite

[1]
Asoke Nath, Swapnil Saha, Himashri Basu, Shalini Torcato, " Visibility Analysis and Time Series Forecasting" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.103-116, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT2390321