“Advanced Cyclone Prediction with Czekanowsky Hyper Graph”

Abstract

Cyclone prediction remains a critical challenge in meteorology due to the complex and nonlinear nature of atmospheric phenomena. This study presents an advanced cyclone prediction framework leveraging Czekanowsky hypergraph-based deep learning techniques, combined with traditional machine learning models for enhanced accuracy and robustness. The proposed approach constructs a hypergraph representation of meteorological data, utilizing the Czekanowsky similarity measure to capture high-order relationships and interactions between multiple atmospheric variables. This hypergraph structure serves as the foundation for a convolutional neural network (CNN) model designed to extract intricate spatiotemporal features for precise cyclone detection and intensity forecasting. Additionally, classical machine learning models, including Random Forest (RF) and K-Nearest Neighbors (KNN), are employed to complement the deep learning framework by providing interpretable insights and comparative performance benchmarks. Experimental results on real-world cyclone datasets demonstrate that the integrated model outperforms individual classifiers in terms of prediction accuracy, precision, and recall. The fusion of hypergraph-based CNN with RF and KNN offers a powerful tool for early cyclone prediction, potentially enhancing disaster preparedness and mitigation strategies.