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.

构建和训练多个卷积神经网络 (CNN) 架构，利用堆叠 CNN 集成方法对地震、洪水、野火和火山等自然灾害图像进行分类，实现了 95% 的准确率和 F1 得分达到 0.96，并通过调节超参数优化单个模型的性能，同时通过将 CNN 和 ResNet 模型与 XGBoost 元模型相结合，提高了分类的整体准确性，展示了基于 CNN 的模型在自动化灾害图像分类中的有效性，为扩展这些技术以构建健壮的灾害响应、损伤评估和恢复管理系统打下了基础。