Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile

Aug 3, 2026

Upload by Noah

This study examines the dynamics of limnological parameters in a South American lake in southern Chile, aiming to predict chlorophyll-a as a key indicator of algal biomass and water quality. Combined remote sensing and machine learning are used to estimate chlorophyll-a at three sampling stations in Lake Ranco. Four models (RNN, LSTM, GRU, TCN) are tested across three data configurations: in situ only, in situ plus meteorological variables, and in situ plus meteorological and satellite data (Landsat/Sentinel).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

Study objective and context

Data and study sites

Methods

Machine learning models

Modeling cases and inputs

Model evaluation

Results

Chlorophyll-a estimation performance

Model comparison across cases

Discussion

Temporal dynamics and ecological implications

Utility for environmental management

Conclusions

remote sensing Article Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile Lien Rodríguez-López 1, *, Lisandra Bravo Alvarez 2, Iongel Duran-Llacer 3,4, David E. Ruíz-Guirola 5, Samuel Montejo-Sánchez 6, Rebeca Martínez-Retureta 7,8, Ernesto López-Morales 1, Luc Bourrel 9, Frédéric Frappart 10 and Roberto Urrutia 11 Citation: Rodríguez-López, L.; Bravo Alvarez, L.; Duran-Llacer, I.; Ruíz-Guirola, D.E.; Montejo-Sánchez, S.; Martínez-Retureta, R.; López-Morales, E.; Bourrel, L.; Frappart, F.; Urrutia, R. Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile. Remote Sens. 2024, 16, 3401. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/rs16183401](10.3390/rs16183401) Academic Editors: Anita Simic-Milasand Yuhong He Received: 2 August 2024 Revised: 5 September 2024 Accepted: 11 September 2024 Published: 13 September 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Lientur 1457, Concepción 4030000, Chile; [ernesto.lopez@uss.cl](ernesto.lopez@uss.cl) 2 Department of Electrical Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción 4030000, Chile; [lisanbravo@udec.cl](lisanbravo@udec.cl) 3 Escuela de Ingeniería en Medio Ambiente y Sustentabilidad, Escuela de Ingeniería Forestal, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Camino La Pirámide 5750, Santiago 8580745, Chile; [iongel.duran@umayor.cl](iongel.duran@umayor.cl) 4 Hémera Centro de Observación de la Tierra, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Camino La Pirámide 5750, Santiago 8580745, Chile 5 Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland; [david.ruizguirola@oulu.fi](david.ruizguirola@oulu.fi) 6 Instituto Universitario de Investigación y Desarrollo Tecnológico, Universidad Tecnológica Metropolitana, Santiago 8940577, Chile; [smontejo@utem.cl](smontejo@utem.cl) 7 Departamento de Ingeniería de Obras Civiles, Facultad de Ingeniería y Ciencias, Universidad de La Frontera, Francisco Salazar 1145, Temuco 4811186, Chile; [rebeca.martinez@ufrontera.cl](rebeca.martinez@ufrontera.cl) 8 Departamento de Ciencias Ambientales, Facultad de Recursos Naturales, Universidad Católica de Temuco, Rudecindo Ortega 02950, Temuco 4780000, Chile 9 Géosciences Environnement Toulouse, UMR 5563, Université de Toulouse, CNRS-IRD-OMP-CNES, 31400 Toulouse, France; [luc.bourrel@ird.fr](luc.bourrel@ird.fr) 10 ISPA, UMR 1391 INRAE, Bordeaux Sciences Agro, UMR 1391, 33140 Villenave-d’Ornon, France; frederic.frappart@inrae.fr 11 Facultad de Ciencias Ambientales, Universidad de Concepción, Concepción 4030000, Chile; [rurrutia@udec.cl](rurrutia@udec.cl) * Correspondence: [lien.rodriguez@uss.cl](lien.rodriguez@uss.cl) Abstract: This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2),

FAQs

What does the research predict for Lake Ranco?

The study predicts chlorophyll-a concentrations, using chlorophyll-a as an indicator of algal biomass and overall water quality dynamics.

Which machine learning models are evaluated in the study?

Four models are used: RNN, LSTM, GRU, and temporal convolutional networks (TCNs).

How do the three cases differ in input data?

Case 1 uses only in situ data, Case 2 combines in situ and meteorological data, and Case 3 adds satellite observations from Landsat and Sentinel together with in situ and meteorological data.

Aug 3, 2026

Upload by Noah