Machine Learning Solutions for Environmental Pollution

Abstract

Environmental studies, notably in fields like environmental forensics, face significant challenges due to uncertainties from environmental changes. In previous decades, many studies attempted to utilize statistical tools to enhance data understanding, but often resulted in limited data interpretation and visualization. Consequently, researchers focused on improving data quality through costly analytical methods. While the costly analytical and instrumental techniques are fundamentally important for ensuring precise qualitative and quantitative information and cannot be overlooked, recent advances in data science, particularly integrating machine learning with large databases, have revolutionized the field. These advancements enable researchers to handle, explore, mine, and predict data with greater accuracy and convenience. Commonly employed machine learning tools include discriminant analysis, support vector machines, neural networks, hyperspectral imaging, hierarchical clustering, and parallel factor analysis. When integrated with advanced sensors and used as field screening devices, machine learning algorithms can enhance pollution response. Properly utilized, these algorithms can also establish a management framework for oil spill fingerprinting and profiling marine contaminants. Additionally, integrated machine learning algorithms are commonly used for source apportionment tasks, including identifying chemical compositions in the characterization of atmospheric substances. In our previous studies, the predictive capabilities of these algorithms not only simplified complex data processing but also elevate the level of data analysis, thus enabling us to anticipate outcomes and respond accordingly.