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The monitoring of natural hazards, such as those affecting the oceans, forests and geological formations, is important for safeguarding human life, protecting property, preserving the ecological balance and promoting technological progress. Research on the prediction and prevention of natural disasters has been conducted for decades in a wide range of areas, and this paper summarise recent research on the use of emerging computer technologies for such prediction, with the expectation that it will serve as a recommendation for future developments in the field. Effective prediction of such disasters depends on the ability to detect anomalous signals in a timely manner. Current methods that rely heavily on manual observation and data analysis are inefficient and prone to human error. Rapid advances in computer science and artificial intelligence technologies offer a promising solution, such as the use of deep learning algorithms or the Spark framework to improve model prediction accuracy and response timeliness. In recent years, computer technology has played a key role in improving these capabilities, contributing to better management of the nations natural resources, and enhancing emergency response strategies. By leveraging the capabilities of deep learning, machine learning and others in data processing and analysis, the field of natural disaster monitoring will continue to evolve towards more efficient and reliable methods.
Chang et al. (Wed,) studied this question.