Author(s): Yumin Kang; Siyoon Kwon; Suhan Nam; Youngdo Kim
Linked Author(s): YongDo Kim
Keywords: Total Phosphorus Total Nitrogen Eutrophication Machine Learning
Abstract: As urban populations and industries concentrate and development zones expand, the conditions within watersheds have undergone significant transformations. Inadequate management of livestock manure and fertilizers in residential sewage systems, livestock farming, and agricultural-industrial complexes has led to a substantial increase in the generation of phosphorus (P) and nitrogen (N). The resulting contamination of aquatic ecosystems with phosphorus and nitrogen has become a critical environmental issue, as it induces eutrophication and poses toxicity risks to aquatic organisms. To mitigate eutrophication, the rapid monitoring of nutrients is essential. However, conventional T-P and T-N monitoring systems predominantly rely on labor-intensive laboratory analyses, which demand considerable time, financial resources, and manpower. To address these limitations, this study proposes a novel approach to predict T-P and T-N concentrations by leveraging water quality parameters measurable through sensors, thereby overcoming the shortcomings of traditional monitoring methods.
Year: 2025