Exploring Exploring Machine Learning and Deep Learning Approaches for Disaster Prediction and Management: A Survey of Different Approaches

Abstract

All around the world, natural disasters have an impact on both human and animal life. In addition to causing major harm to property, animal life, etc. Natural disasters (including landslides, cloudbursts, heat waves, hurricanes, tsunamis, floods, tsunamis, earthquakes, and wildfires) affect thousands of lives each year throughout the world. Twitter, a social networking platform, users can share news, opinions, and personal stories. Due to the widespread availability of real-time data, numerous service agencies regularly analyze this data to spot crises, lower risk, and save lives. Humans, however, are unable to manually filter through the vast number of records and spot hazards in real-time to achieve this, it has been suggested in numerous studies to provide words in forms that computers can understand and on the word representations, use machine learning techniques to determine the meaning behind a post with accuracy. The community can monitor disasters by reporting hazards that are related to disaster occurrences on social media, which has been essential to emergency preparedness. This study examines how social media platform Twitter might be used in disaster-related research. It focuses on the most recent machine learning, deep learning, and disaster prediction techniques. Acquiring a thorough grasp of the numerous data kinds and their sources in relation to a variety of tasks and crisis management scenarios is another goal of the work. Additionally, The study also aims to offer a comprehensive analysis of the various data mining methods utilized for tackling various issues related to natural disasters as well as comprehensive directions on how to categorize tweets as "Related to Catastrophe" or "Not related with Catastrophe" using natural processing methods.