Classification and detection of natural disasters using machine learning and deep learning techniques: A review

For efficient disaster management, it is essential to identify and categorize natural disasters. The classical approaches and current technological advancements for identifying, categorizing, and reducing the harmful effects of natural catastrophes are discussed in this review article. They include human observation and reporting, satellite images, seismology, radar, infrared imagery, and sonar. The article explores natural disasters’ challenges and harmful effects and their mitigation measures. The article explains the benefits and drawbacks of published approaches and emphasizes how they may be used to identify many kinds of natural catastrophes, including earthquakes, floods, wildfires, and hurricanes. Discussions on current technological advancements, including machine and deep learning applications, that can potentially increase the precision and efficiency of natural disaster detection and classification are presented. Overall, the review article emphasizes the significance of continuing research and improving current techniques to increase communities’ and countries’ resilience and preparedness for natural disasters. Moreover, future directions and suggestions to stakeholders in disaster management are highlighted.