Key Physicochemical and Biological Factors Associated with Chlorophyll-a Concentrations: A Machine Learning Approach

Abstract

Chlorophyll-a is a widely used indicator for assessing the trophic status and water quality of aquatic ecosystems because of its close relationship with phytoplankton biomass. This study aimed to identify the physicochemical, biological, and temporal variables associated with chlorophyll-a concentrations in Lake Gatun during the 2017–2023 period using the Light Gradient Boosting Machine (LightGBM) algorithm and a Generalized Linear Model (GLM). A total of 25 predictor variables describing water quality were analyzed using data collected from 14 monitoring stations distributed throughout Lake Gatun within the Panama Canal watershed. The LightGBM model achieved satisfactory predictive performance, with an RMSE of 4.58, an MAE of 3.26, and a coefficient of determination (R2) of 0.42 on the testing dataset. Variable importance analysis identified turbidity, dissolved oxygen, and water transparency as the most influential predictors of chlorophyll-a concentrations. These findings improve our understanding of the factors associated with chlorophyll-a variability and demonstrate the potential of machine learning models as decision-support tools for monitoring and managing aquatic ecosystems.

Carlos Alfredo Torres Cubilla

Data Scientist

My interests focus on programming, statistics and data science.