| Title | Machine Learning Algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile |
| Abstract | The world's water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources. |
| Source | Water (Basel) 15 (11), pp. 1–21 |
| Keywords | machine learning algorithmschlorophyll-alake |
| Journal | Water (Basel) |
| Editor | Molecular Diversity Preservation International, Basel, |
| Year | 2023 |
| Type | Articolo in rivista |
| DOI | 10.3390/w15111994 |
| Authors | Rodriguez-Lopez, Lien; Bustos Usta, David; Bravo Alvarez, Lisandra; Duran-Llacer, Iongel; Lami, Andrea; Martinez-Retureta, Rebeca; Urrutia, Roberto |
| Text | 487704 2023 10.3390/w15111994 ISI Web of Science WOS 001006538000001 machine learning algorithms chlorophyll a lake Machine Learning Algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile Rodriguez Lopez, Lien; Bustos Usta, David; Bravo Alvarez, Lisandra; Duran Llacer, Iongel; Lami, Andrea; Martinez Retureta, Rebeca; Urrutia, Roberto Univ San Sebastian; Univ Concepcion; Univ Concepcion; Univ Mayor; Inst Water Res IRSA; Univ Concepcion The world s water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31 year time series 1989 to 2020 of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll a, three models showed better performance XGBoost, LightGBM, and AdaBoost . The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root mean square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources. 15 Published version Articolo in rivista Molecular Diversity Preservation International 2073 4441 Water Basel Water Basel Water Basel Water. Basel andrea.lami LAMI ANDREA |
