Abstract
The term environmental monitoring refers to the practice of keeping tabs on and assessing the state of both natural and built environments. The purpose of environmental monitoring is to gather information that may be utilized to spot patterns, hazards, and improvement avenues. Because they can analyse enormous volumes of data and identify complicated patterns that may not be clearly detectable using conventional methods, machine learning techniques may be particularly successful for environmental monitoring. The lack of a reliable method to gather complete data, and the overall lack of data openness, is the main problem with the status quo. These environmental data are often collected in siloed units, requiring time and money from environmental protection agencies before they can be made public. In this study, we will look at how machine learning may be put to use in environmental surveillance. Two recent cases will be discussed briefly within the framework of our paper before we wrap things up.
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