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International Journal of Innovations & Research Analysis (IJIRA) [ Vol. 6 | No. 3(II) | July - September, 2026 ]

Trend Analysis for Frequency of Cyclonic Systems in Indian Ocean Region during Pre-Monsoon Season by AIML

Sumana Chatterjee

This paper is based on research study with trend analysis of the frequency of occurrence of cyclonic systems in Indian Ocean during pre-monsoon season. The analysis is based on Artificial Intelligence and Machine Learning. The research interest of this study is developed for the purpose of understanding the trend from historical past till present time along with prediction of trend for near future years, to realise whether there is any significant change of trend for near future years will take place or not in comparison with historical years. The data source is online data supply platform IMD DSP PUNE. The data with frequency of cyclonic system since the historical past year 1891, consisting of frequency of cyclonic disturbances, cyclones and severe cyclonic storms in Indian Ocean region, has been obtained for data analysis. For each type of system, developed in each type of source, i.e. systems developed in Bay of Bengal, Arabian sea and Land, during pre-monsoon season ,span March to May ,data analysis with dataset of frequency of occurrence based on models Mann-Kendall test for monotonic trend, ARIMA time series and Neural network ,by AIML python execution on google collaborator platform has been done to visualize the trend from historical past to near future years. Accuracy of the output has been verified with comparison between these models.

Chatterjee, S. (2026). Trend Analysis for Frequency of Cyclonic Systems in Indian Ocean Region during Pre-Monsoon Season by AIML. International Journal of Innovations & Research Analysis, 06(03(II)), 51–59. https://doi.org/10.62823/IJIRA/06.3(II).9499
  1. R. C. (2020). Machine Learning in Tropical Cyclone Forecast Modeling: A Review. MDPI, 11(7), 1-29. doi:https://doi.org/10.3390/atmos11070676
  2. Richmana, M. B. (2012). Adaptive Machine Learning Approaches to Seasonal Prediction of Tropical. Science Direct, 276-281. doi:doi: 10.1016/j.procs.2012.09.069
  3. Richmana, M. B. (2017). Reducing Tropical Cyclone Prediction Errors Using Machine Learning Approaches. Science direct, 314-323. doi:https://doi.org/10.1016/j.procs.2017.09.048
  4. Nath, S. (2015). Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model. ATMOSFERA, 271-281. doi:doi: 10.20937/ATM.2015.28.04.06
  5. QiFeng Qian1, 2. X. (2022). Reduced Tropical Cyclone Genesis in the Future as Predicted by a Machine Learning Model. ADVANCING EARTH AND SPACE SCIENCE, 1-8. doi: https://doi.org/10.1029/2021EF002455.

DOI:

Article DOI: 10.62823/IJIRA/06.3(II).9499

DOI URL: https://doi.org/10.62823/IJIRA/06.3(II).9499

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