  Natural disasters act as a serious threat globally, requiring effective and
efficient disaster management and recovery. This paper focuses on classifying
natural disaster images using Convolutional Neural Networks (CNNs). Multiple
CNN architectures were built and trained on a dataset containing images of
earthquakes, floods, wildfires, and volcanoes. A stacked CNN ensemble approach
proved to be the most effective, achieving 95% accuracy and an F1 score going
up to 0.96 for individual classes. Tuning hyperparameters of individual models
for optimization was critical to maximize the models' performance. The stacking
of CNNs with XGBoost acting as the meta-model utilizes the strengths of the CNN
and ResNet models to improve the overall accuracy of the classification.
Results obtained from the models illustrated the potency of CNN-based models
for automated disaster image classification. This lays the foundation for
expanding these techniques to build robust systems for disaster response,
damage assessment, and recovery management.
