Water Resources Research Act Program

Details for Project ID ND-2024-Lim-2

Enhancing Water Quality Monitoring Through Regional Machine Learning Models

Institute: North Dakota
Year Established: 2024 Start Date: 2024-09-01 End Date: 2025-08-31
Total Federal Funds: $9,393 Total Non-Federal Funds: $9,514

Principal Investigators: Yeo Howe Lim

Project Summary: Water quality monitoring is essential for safeguarding freshwater ecosystems and human health. This project aims to investigate the efficacy of regional machine learning models, trained on hyperspectral data collected across diverse geographical locations, in predicting phosphate concentrations compared to site-specific models. Leveraging hyperspectral remote sensing technology and machine learning algorithms, the study aims to develop predictive models capable of estimating phosphate concentrations in water bodies. By integrating spectral analysis, feature extraction, and machine learning techniques, the project seeks to identify informative spectral signatures associated with phosphate levels. The research will involve three main phases: data collection and preprocessing, model development and calibration, and model validation and performance analysis, with a primary emphasis on machine learning techniques. The findings will contribute to advancing water quality monitoring practices and informing evidence-based management strategies for freshwater resources.