Spatiotemporal modeling of annual diffuse flow-weighted total phosphorus concentrations in Danish headwater streams with machine learning.

ABSTRACT: High total phosphorus (TP) concentrations in freshwater such as streams, rivers, and lakes remain a global issue, causing eutrophication and poor ecological conditions. We extracted annual flow-weighted TP concentration data (N = 3144) from 207 monitored Danish headwater streams (catchment area < 50 km²) during the period 1990-2019 as the basis for developing a machine learning (ML) model for diffuse phosphorus in streams, using 22 potential predictor variables. We tested 70 different algorithms, applying an AI platform and a random split strategy with a holdout dataset (20%), a validation dataset (16%), and four training datasets (4 × 16%) in a fivefold cross-validation procedure across a total of 207 stream stations. The ML algorithm "eXtreme Gradient Boosted trees regressor:

SUBMITTER: Kronvang B 

PROVIDER: S-EPMC12859753 | biostudies-literature | 2026 Jan-Feb

REPOSITORIES: biostudies-literature