A Comprehensive Investigation of Underwater Disaster Prediction Using Machine Learning Algorithms

Abstract

Underwater disasters cause severe consequences to marine ecology and lead to significant loss to the environment. Since the sea water temperature reaches 700° Fahrenheit, disasters like hydrothermal vents and underwater volcano eruptions occur. Due to these seireachests, marine creatures like sharks and different types of living species have been excavating under the ocean. The risk of underwater disasters like underwater volcanic eruption, tsunami, underwater earthquake, submarine accidents and oil spills are crucial, and the preventive measures of these calamities were highly needed. Oceanographic data includes underwater images and videos captured bymarine archaeologists and divers to know the information about the exploited resources. Existing traditional algorithms have practical limitations to predict underwater catastrophes under more depth condition and can be overcome by machine learning (ML) algorithms, since it was accurate and fast in analyzing the oceanographic data. This chapter provides a comprehensive review of predicting underwater disasters using ML algorithms.