Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation.

ABSTRACT: High-frequency monitoring of water quality in catchments brings along the challenge of post-processing large amounts of data. Moreover, monitoring stations are often remote and technical issues resulting in data gaps are common. Machine learning algorithms can be applied to fill these gaps, and to a certain extent, for predictions and interpretation. The objectives of this study were (1) to evaluate six different machine learning models for gap-filling in a high-frequency nitrate and total phosphorus concentration time series, (2) to showcase the potential added value (and limitations) of machine learning to interpret underlying processes, and (3) to study the limits of machine learning algorithms for predictions outside the training period. We used a 4-year high-frequency dataset from a d

SUBMITTER: Barcala V 

PROVIDER: S-EPMC10299926 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature