Machine Learning Tackles Small-Data Challenges in Aquatic Environments
Chinese Academy of Sciences
Machine learning (ML) has emerged as a promising tool for tackling challenges in aquatic environmental research, especially in small-data scenarios where limited sample sizes and high dimensionality typically hinder model performance. This review assesses current ML approaches applied to small datasets in aquatic systems, focusing on data characteristics, modeling strategies, and optimization frameworks. By examining the structural features of aquatic datasets, the review highlights key methodol