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dc.contributor.author Rodríguez-López, Lien
dc.contributor.author Usta, David Bustos
dc.contributor.author Duran-Llacer, Iongel
dc.contributor.author Alvarez, Lisandra Bravo
dc.contributor.author Yépez, Santiago
dc.contributor.author Bourrel, Luc
dc.contributor.author Frappart, Frederic
dc.contributor.author Urrutia, Roberto
dc.date.accessioned 2024-09-26T00:46:43Z
dc.date.available 2024-09-26T00:46:43Z
dc.date.issued 2023-09
dc.identifier.issn 2072-4292
dc.identifier.uri https://repositorio.uss.cl/handle/uss/13495
dc.description Funding Information: This research was funded by CRHIAM (ANID/FONDAP/15130015) and with the collaboration of the Chilean government through ANID’s Fondecyt Regular Project 1221091. Funding Information: L.R.-L. is grateful to the Centro de Recursos Hídricos para la Agricultura y la Minería (CRHIAM) (Project ANID/FONDAP/15130015) and S.Y. is grateful for ANID’s support through the Fondecyt Regular Project 1221091. Publisher Copyright: © 2023 by the authors.
dc.description.abstract In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality. en
dc.language.iso eng
dc.relation.ispartof vol. 15 Issue: no. 17 Pages:
dc.source Remote Sensing
dc.title Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile en
dc.type Artículo
dc.identifier.doi 10.3390/rs15174157
dc.publisher.department Facultad de Ingeniería y Tecnología
dc.publisher.department Facultad de Ingeniería, Arquitectura y Diseño


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