Machine Learning Algorithms for Disaster Detection and Management

Abstract

Every year, various natural calamities and catastrophes, such as earthquakes, landslides, floods, and typhoons, significantly impact extensive regions across the globe, mainly residential areas and artificial structures such as buildings. To mitigate the adverse effects of these events, it is crucial to have timely access to precise and up-to-date geospatial information about the affected areas, especially during the initial phases. This paper presents a thorough examination of various machine learning and deep learning techniques that have been employed in the management and prediction of natural disasters. These algorithms have been applied in multiple disaster management activities, such as forecasting the timing and locations of crowd evacuations, analyzing social media content, and overseeing sustainable development initiatives. The methodology outlined in this research paper involves a three-step process: identifying landmarks in images, training a machine-learning model, and categorizing the images. The article commences by providing a tutorial on Machine Learning (ML) algorithms, as ML algorithms can effectively process vast amounts of multi-dimensional data commonly encountered in disaster and pandemic management scenarios. These algorithms are especially adept at performing crucial tasks like recognition and classification. In this research, deep learning models were utilized to extract deep features from images to identify distinct types of disasters. These features were then classified using machine learning techniques commonly found in existing literature. The proposed methodology presents the utilization of deep learning algorithms for identifying impacted regions, specifically buildings. Furthermore, a contemporary framework is suggested, merging images captured by Unmanned Aerial Vehicles (UAVs) with deep learning algorithms to enable rapid disaster mapping. Additionally, this text delves into many challenges, unresolved matters, and potential avenues for future investigation.