[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120101-en":3,"doc-seo-120101-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120101,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile","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).","remote sensing  \nArticle  \nLeveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile  \nLien 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  \nCitation: Rodríguez-López, L.; Bravo Alvarez, L.; Duran-Llacer, I.;  \nRuíz-Guirola, D.E.; Montejo-Sánchez, S.; Martínez-Retureta, R.;  \nLópez-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)  \nAcademic Editors: Anita Simic-Milasand Yuhong He  \nReceived: 2 August 2024  \nRevised: 5 September 2024  \nAccepted: 11 September 2024  \nPublished: 13 September 2024  \nCopyright: © 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/)) .  \n1 Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Lientur 1457, Concepción 4030000, Chile; [ernesto.lopez@uss.cl](ernesto.lopez@uss.cl)  \n2 Department of Electrical Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción 4030000, Chile; [lisanbravo@udec.cl](lisanbravo@udec.cl)  \n3 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)  \n4 Hémera Centro de Observación de la Tierra, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Camino La Pirámide 5750, Santiago 8580745, Chile  \n5 Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland; [david.ruizguirola@oulu.fi](david.ruizguirola@oulu.fi)  \n6 Instituto Universitario de Investigación y Desarrollo Tecnológico, Universidad Tecnológica Metropolitana, Santiago 8940577, Chile; [smontejo@utem.cl](smontejo@utem.cl)  \n7 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)  \n8 Departamento de Ciencias Ambientales, Facultad de Recursos Naturales, Universidad Católica de Temuco, Rudecindo Ortega 02950, Temuco 4780000, Chile  \n9 Géosciences Environnement Toulouse, UMR 5563, Université de Toulouse, CNRS-IRD-OMP-CNES,  \n31400 Toulouse, France; [luc.bourrel@ird.fr](luc.bourrel@ird.fr)  \n10 ISPA, UMR 1391 INRAE, Bordeaux Sciences Agro, UMR 1391, 33140 Villenave-d’Ornon, France;  \nfrederic.frappart@inrae.fr  \n11 Facultad de Ciencias Ambientales, Universidad de Concepción, Concepción 4030000, Chile; [rurrutia@udec.cl](rurrutia@udec.cl)  \n* Correspondence: [lien.rodriguez@uss.cl](lien.rodriguez@uss.cl)  \nAbstract: 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), ","cbCailQqWyNDLkFI","https://ap.wps.com/l/cbCailQqWyNDLkFI","pdf",12058503,1,20,"English","en",105,"# Introduction\n## Study objective and context\n## Data and study sites\n# Methods\n## Machine learning models\n## Modeling cases and inputs\n## Model evaluation\n# Results\n## Chlorophyll-a estimation performance\n## Model comparison across cases\n# Discussion\n## Temporal dynamics and ecological implications\n## Utility for environmental management\n# Conclusions","[{\"question\":\"What does the research predict for Lake Ranco?\",\"answer\":\"The study predicts chlorophyll-a concentrations, using chlorophyll-a as an indicator of algal biomass and overall water quality dynamics.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"Four models are used: RNN, LSTM, GRU, and temporal convolutional networks (TCNs).\"},{\"question\":\"How do the three cases differ in input data?\",\"answer\":\"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.\"}]","Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile | 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