ItemConference Paper

A Hybrid Deep Learning and Ensemble Framework for Real-Time Cyclone Path and Intensity Prediction in Disaster-Prone Regions

Title
A Hybrid Deep Learning and Ensemble Framework for Real-Time Cyclone Path and Intensity Prediction in Disaster-Prone Regions
Creator
Paul, P. Mano; Praveen, R.V.S.; Vemuri, Harikrishna; John, Tegil J; Manoj Senthil, K.; Muralidharan, A.
Description
Predicting the path and strength of cyclones involves significant issues in meteorology, as mistakes can greatly affect disaster management and evacuation strategies. Current models frequently encounter difficulties in achieving accurate real-time forecasting, particularly in representing complicated spatial-temporal dynamics of cyclones. The proposed study presents an innovative hybrid architecture combining deep learning and ensemble methods, using convolutional layers, LSTM units, and a gradient boosting meta-learner to improve prediction efficiency. The system was trained and verified utilising multi-year cyclone datasets obtained from Kaggle, which included atmospheric and oceanic factors. The model architecture attained exceptional accuracy, with a track error of 28 km, a mean absolute error (MAE) of 3.2 hPa for pressure, 4.5 km/h for wind speed, and a root mean square error (RMSE) of 35.4 km. The suggested approach consistently outperformed baseline models, including ConvLSTM, GRU, and XGBoost, across all critical criteria. The deployment in real-time was enabled by a containerised, low-latency API that can integrate with disaster early warning systems. This research enhances cyclone forecasting by offering a scalable, precise, and operationally feasible solution for disaster-prone areas, demonstrating practical superiority over current methodologies. The results highlight the capability of hybrid AI models to improve the accuracy and dependability of meteorological forecasts. 2025 IEEE.
Source
Proceedings of the 6th International Conference on Smart Electronics and Communication, ICOSEC 2025;pp.1825-1830
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Atmospheric Data Analysis; Convolutional Neural Networks; Cyclone Prediction; Deep Learning; Disaster Management; Ensemble Learning; Gradient Boosting; Hybrid Model; LSTM; Meteorology AI; Real-Time Forecasting; RMSE Cyclone Path; Spatial-Temporal Modelling; Track Error Reduction; XGBoost
Coverage
Paul P.M., Dayananda Sagar Academy Of Technology And Management, Department Of CSE-Artificial Intelligence, Karnataka, India; Praveen R.V.S., LTIMindtree, Department Of Utilities (Director, Utilities America), Houston, TX, United States; Vemuri H., Deloitte Consulting Llp, Department Of Enterprise Performance, Houston, TX, United States; John T.J., Christ University, Department Of Computer Science, Karnataka, Benguluru, India; Manoj Senthil K., Kongu Engineering College, Department Of Electronics And Communication Engineering, Erode, India; Muralidharan A., Hindusthan Institute Of Technology, Department Of Computer Science And Engineering, Tamil Nadu, Coimbatore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159859-4;
Format
online
Language
English
Type
Conference paper

Citation

Paul, P. Mano; Praveen, R.V.S.; Vemuri, Harikrishna; John, Tegil J; Manoj Senthil, K.; Muralidharan, A., “A Hybrid Deep Learning and Ensemble Framework for Real-Time Cyclone Path and Intensity Prediction in Disaster-Prone Regions,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/26079.

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