Advances in Cyclone Intensity Classification: A Comparative Analysis

Apart from serving as an ulterior discussion-topic, cyclone classification serves to provide timely information to decision-makers for timely intervention. The majority of traditional methods, such as ADT, leaning on interpreting this in manual ways, which leads to inconsistencies. Convolutional Neural Networks (CNNs) are a new breed of machine learning models most suited for the tasks of automated feature extraction, ideal for both cyclone detection and intensity determination. In the context of this review, both binary and multi-class techniques for cyclone classification are discussed, along with the problems of data set issues, computational restrictions, and real-world deployment challenges. Major limitations include class imbalance, misclassifications of classes with adjacent intensity difference, and delays in forecast for urgent decision-making. Techniques such as data augmentation, multispectral image incorporation, and cost-sensitive learning are elaborated upon as aids in dealing with the above-listed issues and concerns. Further research should concentrate on real-time processing, efficient architectures, and scalable solutions for enhanced cyclone classification accuracy and operational feasibility. This review would help understand current trends and challenges and guide researchers in developing robust AI-based cyclone classification models for meteorology and disaster management.