UAV‐MCND: A Novel System for Multiclass Natural Disaster Classification Using FusionNet‐4 and Water Wheel‐Guided Walrus Optimization

Natural disasters are one of the biggest challenges for response operations. Their detection may need advanced and accurate detection technologies. Therefore, a novel UAV‐based multiclass natural disaster classification system with the integration of FusionNet‐4 architecture and water wheel‐guided walrus optimization (WWGWO) algorithm is proposed. The goal is to have a comprehensive and adaptive framework that may be used in identifying and classifying disaster scenarios accurately. The system has six major phases, which include image acquisition, preprocessing, segmentation, feature extraction, feature selection, and classification. The key innovation is the FusionNet‐4 ensemble‐based model, which employs ResNet‐50, DenseNet‐121, VGG‐19, and EfficientNet CNN architectures with the functionalities of multilevel feature extraction to increase the accuracy of disaster classification. The study proposes a method for automated natural disaster classification using UAV imagery, utilizing advanced deep learning and metaheuristic optimization techniques for swift and precise disaster response. Furthermore, an optimized UNet segmentation strategy, fine‐tuned using the hybrid WWGWO algorithm to achieve exploration and exploitation for efficient feature selection and superior segmentation quality, is proposed. Experimental testing on high‐resolution disaster datasets, such as RescueNet and xView2, has validated the proposed model. FusionNet‐4 architecture performs better than conventional CNNs, with an MSE of 0.0135 for an 80:20 training‐to‐testing data‐split ratio at a learning rate of 0.001, giving it better accuracy of 98.93% in classification and adaptability. Optimal feature selection has been ensured through the integration of the WWGWO algorithm, reducing computational complexity and improving overall efficiency.