A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Datasets and Preprocessing
2.2.1. Field Data
2.2.2. Satellite Data
2.2.3. Meteorological Data
3. Methods
3.1. Framework for TSI Estimation Model
3.2. Quantification of Trophic State
3.3. Data Preprocessing
3.3.1. TSI Outlier Handling
3.3.2. Preprocessing of RS Images
3.3.3. Extracting Water Bodies
3.4. Estimation Modeling Techniques
3.4.1. Selection of Environmental Factors
3.4.2. TSI Estimation Model Based on Backpropagation Neural Network
3.4.3. TSI Estimation Model Based on Backpropagation Neural Network
3.4.4. Assessment of the Accuracy of the Model
4. Results
4.1. TSI Level and S-2 Spectral Characteristics
4.2. TSI Estimation Using Environmental Factors
4.2.1. Comparison of the Performances of the TSI Estimation Model with Environmental Factors
4.2.2. Mean Impact Value Analysis
4.3. Temporal and Spatial Distribution of Trophic State
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
No. | Hidden Layer Size | R2 | RMSE | MAPE |
---|---|---|---|---|
1 | 5 | 0.8353 | 4.7017 | 4.9326 |
2 | 6 | 0.8260 | 4.8652 | 5.8568 |
3 | 7 | 0.7332 | 6.0059 | 7.6803 |
4 | 8 | 0.8490 | 4.5014 | 4.5445 |
5 | 9 | 0.8401 | 4.6384 | 4.7116 |
6 | 10 | 0.8417 | 4.6497 | 3.8771 |
7 | 11 | 0.9104 | 3.4849 | 3.7089 |
8 | 12 | 0.9220 | 3.2559 | 2.4944 |
9 | 13 | 0.8211 | 5.0092 | 2.5062 |
10 | 14 | 0.8030 | 5.2856 | 3.5363 |
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No. | Input Variables | Description of Water Conditions |
---|---|---|
1 | Rrs only | Typical RS estimation method |
2 | T & Rrs | TSI under the action of air temperature |
3 | WT & Rrs | TSI under the action of water temperature |
4 | WD & (WT/T) & Rrs | TSI under the combined action of temperature and wind direction |
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Zhu, S.; Mao, J. A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors. Remote Sens. 2021, 13, 2498. https://doi.org/10.3390/rs13132498
Zhu S, Mao J. A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors. Remote Sensing. 2021; 13(13):2498. https://doi.org/10.3390/rs13132498
Chicago/Turabian StyleZhu, Shijie, and Jingqiao Mao. 2021. "A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors" Remote Sensing 13, no. 13: 2498. https://doi.org/10.3390/rs13132498