Year: 2026
Type: article
Abstract: Freshwater is essential for sustaining ecosystems and economic activities, and water quality largely determines its potential uses. Among water quality parameters, turbidity is particularly important due to its direct influence on aquatic ecosystem health, water resource management, and human well-being. This study investigates the long-term water quality and quantity dynamics of Lake Chapo, southern Chile, with the aim of assessing its evolution over the past 20 years. Thirteen machine learning models were evaluated to estimate turbidity under three scenarios: (1) using in situ limnological variables, (2) using meteorological variables, and (3) using the combined dataset. Overall, all models exhibited strong predictive performance. The best results were obtained with the multilayer perceptron model (R² = 0.78, MAE = 0.69, RMSE = 0.95), the Gaussian process model (R² = 0.75, RMSE = 0.78, MAE = 0.97 across all cases), and the random forest model (R² = 0.75, RMSE = 0.81, MAE = 0.96 in Case 1). Correlation analyses revealed that precipitation and wind speed exert the strongest meteorological influence on turbidity variability. These findings demonstrate that combining machine learning techniques with environmental data enhances understanding of aquatic ecosystem dynamics and supports improved freshwater resource management.
Institutions: San Sebastián University, University of Concepción, University of Liège, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, École Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine, Géosciences Environnement Toulouse, Université de Toulouse
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,060
Awards: G1197213904, G1507641020, G1902165130, G2079469327, G2091587161, G3350487818, OPTIMIZANDO LA EVALUACION HIDRICA DE UN ECOSISTEMA ACUATICO EN LA PATAGONIA CHILENA MEDIANTE ALTIMETRIA SATELITAL (OPTIMIZING THE WATER ASSESSMENT OF AN AQUATIC ECOSYSTEM IN CHILEAN PATAGONIA USING SATELLITE ALTIMETRY)._X000D_, G5099478859, G5650031767, WATER RESEARCH CENTER FOR AGRICULTURE AND MINING (CRHIAM), INTEGRATING BIO-OPTICAL MODELING AND REMOTE SENSING FOR ENHANCED UNDERSTANDING OF ALGAL BLOOMS IN INLAND WATERS, CENTRO DE RECURSOS HIDRICOS PARA LA AGRICULTURA Y MINERIA (CRHIAM)
OpenAlex ID: W7124944462