Kishore et al. (2026) Prediction of Indian Tropical Cyclones Using Machine Learning Techniques

Identification

• Journal: Lecture notes in networks and systems
• Year: 2026
• Date: 2026-01-01
• Authors: Anish Kishore, Pratyush Ranjan, Adhyyan Tulsyan, Harswardhan Khandelwal, Swaraj Thakur, Vandana Bhattacharjee
• DOI: 10.1007/978-3-032-13544-5_27

Research Groups

Birla Institute of Technology, Mesra, Ranchi, India

Short Summary

This paper aims to apply machine learning techniques for the prediction of Indian Tropical Cyclones to reduce uncertainty and aid disaster management authorities in planning mitigation processes.

Objective

• To develop and apply machine learning techniques for the prediction of Indian Tropical Cyclones to enhance disaster management and mitigation planning.

Study Configuration

• Spatial Scale: Indian Ocean region (specifically for Indian Tropical Cyclones).
• Temporal Scale: Not explicitly detailed, but concerns the prediction of future tropical cyclone events.

Methodology and Data

• Models used: Machine Learning Techniques (specific models not detailed in the provided text).
• Data sources: Not explicitly detailed in the provided abstract/preview.

Main Results

• The main results are not provided in this preview of the conference paper.

Contributions

• The article contributes by exploring the application of machine learning techniques to improve the prediction of Indian Tropical Cyclones, aiming to reduce uncertainty for disaster management authorities.

Funding

• No funding information is provided in the text.

Citation

@article{Kishore2026Prediction,
  author = {Kishore, Anish and Ranjan, Pratyush and Tulsyan, Adhyyan and Khandelwal, Harswardhan and Thakur, Swaraj and Bhattacharjee, Vandana},
  title = {Prediction of Indian Tropical Cyclones Using Machine Learning Techniques},
  journal = {Lecture notes in networks and systems},
  year = {2026},
  doi = {10.1007/978-3-032-13544-5_27},
  url = {https://doi.org/10.1007/978-3-032-13544-5_27}
}