Skip to main content
U.S. flag

An official website of the United States government

Examining the effect of physicochemical and meteorological variables on water quality indicators of harmful algal blooms in a shallow hypereutrophic lake using machine learning techniques

January 24, 2024

Two independent machine learning techniques, boosted regression trees and artificial neural networks, were used to examine the physicochemical and meteorological variables that affect the seasonal growth and decline of harmful algal blooms (HABs) in a shallow, hypereutrophic lake in southern Oregon. High temporal resolution data collected at four monitoring locations were aggregated into daily timesteps to create two response variables: (1) daily maximum pH (pHmax), representing HAB growth, and (2) daily minimum dissolved oxygen (DOmin), representing HAB decline. Predictors included meteorological and physical data, estimates of external phosphorus loading, and previous-year average nutrient concentrations, and excluded HAB biomass and internal phosphorus loading. The predictors that captured seasonal changes in both pHmax and DOmin were temperature, inflows, lake-surface elevation, and external phosphorus loading, while short-term changes were captured by measures of stratification, temperature, and wind speed. The pHmax models had similar fits with leave-one-year-out cross-validation (LOYO-CV) R2 values of 0.2–0.43 (median = 0.40). The DOmin models for the deeper locations had LOYO-CV R2 values of 0.27–0.43 compared to 0.1–0.25 for the shallower locations. Model performance was affected by variability due to patchiness of HABs, measurement uncertainty, and advection.

Publication Year 2024
Title Examining the effect of physicochemical and meteorological variables on water quality indicators of harmful algal blooms in a shallow hypereutrophic lake using machine learning techniques
DOI 10.1021/acsestwater.3c00299
Authors Susan Wherry, Liam N. Schenk
Publication Type Article
Publication Subtype Journal Article
Series Title Water
Index ID 70251161
Record Source USGS Publications Warehouse
USGS Organization Oregon Water Science Center
Was this page helpful?