A REVIEW OF MACHINE LEARNING TECHNIQUES APPLICATIONS IN ENVIRONMENTAL SCIENCE

Abstract

Advances in Machine Learning (ML) and Data Science (DS) hold immense potential to transform various aspects of environmental science (ES). DS, a broad field focused on extracting insights from data using techniques like statistical analysis plays a crucial role in this process. Machine learning, on the other hand, specializes in creating algorithms that enable computers to learn from data and make predictions. Together, these technologies can deepen our understanding of complex environmental systems, refine predictive models for climate change, support conservation efforts, and optimize resource management practices. Such scientific discovery will enhance ES to make autonomous, real-time decisions by deriving valuable insights from extensive data. By analysing large datasets, machine learning algorithms can reveal hidden patterns and insights, empowering scientists to make data-driven decisions and tackle environmental challenges more effectively. This article offers a review of the fundamental concepts of Machine Learning, Deep Learning, and Data Analytics for two groups: individuals familiar with ML who seek to expand their knowledge, and domain scientists passionate about integrating these transformative tools into their research in the environmental science profession.