Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico
Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a signif...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/23/5505 |
| _version_ | 1797399672717836288 |
|---|---|
| author | Freddy Hernán Villota-González Belkis Sulbarán-Rangel Florentina Zurita-Martínez Kelly Joel Gurubel-Tun Virgilio Zúñiga-Grajeda |
| author_facet | Freddy Hernán Villota-González Belkis Sulbarán-Rangel Florentina Zurita-Martínez Kelly Joel Gurubel-Tun Virgilio Zúñiga-Grajeda |
| author_sort | Freddy Hernán Villota-González |
| collection | DOAJ |
| description | Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r<sup>2</sup> = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r<sup>2</sup> = 0.60, RMSE = 48.06 mg/m<sup>3</sup>, MAE = 37.98 mg/m<sup>3</sup>), and TSS (r<sup>2</sup> = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r<sup>2</sup> = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r<sup>2</sup> = 0.71, RMSE = 6.16 mg/m<sup>3</sup>, MAE = 4.97 mg/m<sup>3</sup>) with Landsat-8 radiometric data, while TSS (r<sup>2</sup> = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r<sup>2</sup> values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems. |
| first_indexed | 2024-03-09T01:44:29Z |
| format | Article |
| id | doaj.art-e4a90e511d7a4275aca1c6f9b9423c96 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T01:44:29Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e4a90e511d7a4275aca1c6f9b9423c962023-12-08T15:24:50ZengMDPI AGRemote Sensing2072-42922023-11-011523550510.3390/rs15235505Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—MexicoFreddy Hernán Villota-González0Belkis Sulbarán-Rangel1Florentina Zurita-Martínez2Kelly Joel Gurubel-Tun3Virgilio Zúñiga-Grajeda4Department of Water and Energy, University of Guadalajara, Campus Tonalá, Tonalá 45425, MexicoDepartment of Water and Energy, University of Guadalajara, Campus Tonalá, Tonalá 45425, MexicoEnvironmental Quality Research Center, University of Guadalajara, Campus Ciénega, Ocotlán 47810, MexicoDepartment of Water and Energy, University of Guadalajara, Campus Tonalá, Tonalá 45425, MexicoInformation Sciences and Technological Development, University of Guadalajara, Campus Tonalá, Tonalá 45425, MexicoRemote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititlán and Zapotlán lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititlán, these models provided the most accurate predictions for turbidity (r<sup>2</sup> = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r<sup>2</sup> = 0.60, RMSE = 48.06 mg/m<sup>3</sup>, MAE = 37.98 mg/m<sup>3</sup>), and TSS (r<sup>2</sup> = 0.68, RMSE = 13.42 mg/L, MAE = 10.36 mg/L) when using radiometric data from Landsat-8. In Lake Zapotlán, better predictive performance was observed for turbidity (r<sup>2</sup> = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r<sup>2</sup> = 0.71, RMSE = 6.16 mg/m<sup>3</sup>, MAE = 4.97 mg/m<sup>3</sup>) with Landsat-8 radiometric data, while TSS (r<sup>2</sup> = 0.72, RMSE = 2.71 mg/L, MAE = 2.12 mg/L) improved when Sentinel-2 data were employed. While r<sup>2</sup> values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model’s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems.https://www.mdpi.com/2072-4292/15/23/5505machine learning algorithmsin situ water quality datalakesLandsat-8Sentinel-2 |
| spellingShingle | Freddy Hernán Villota-González Belkis Sulbarán-Rangel Florentina Zurita-Martínez Kelly Joel Gurubel-Tun Virgilio Zúñiga-Grajeda Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico Remote Sensing machine learning algorithms in situ water quality data lakes Landsat-8 Sentinel-2 |
| title | Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico |
| title_full | Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico |
| title_fullStr | Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico |
| title_full_unstemmed | Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico |
| title_short | Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititlán and Zapotlán, Jalisco—Mexico |
| title_sort | assessment of machine learning models for remote sensing of water quality in lakes cajititlan and zapotlan jalisco mexico |
| topic | machine learning algorithms in situ water quality data lakes Landsat-8 Sentinel-2 |
| url | https://www.mdpi.com/2072-4292/15/23/5505 |
| work_keys_str_mv | AT freddyhernanvillotagonzalez assessmentofmachinelearningmodelsforremotesensingofwaterqualityinlakescajititlanandzapotlanjaliscomexico AT belkissulbaranrangel assessmentofmachinelearningmodelsforremotesensingofwaterqualityinlakescajititlanandzapotlanjaliscomexico AT florentinazuritamartinez assessmentofmachinelearningmodelsforremotesensingofwaterqualityinlakescajititlanandzapotlanjaliscomexico AT kellyjoelgurubeltun assessmentofmachinelearningmodelsforremotesensingofwaterqualityinlakescajititlanandzapotlanjaliscomexico AT virgiliozunigagrajeda assessmentofmachinelearningmodelsforremotesensingofwaterqualityinlakescajititlanandzapotlanjaliscomexico |