Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River
The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/119 |
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| author | Manqi Wang Caili Zhou Jiaqi Shi Fei Lin Yucheng Li Yimin Hu Xuesheng Zhang |
| author_facet | Manqi Wang Caili Zhou Jiaqi Shi Fei Lin Yucheng Li Yimin Hu Xuesheng Zhang |
| author_sort | Manqi Wang |
| collection | DOAJ |
| description | The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH<sub>3</sub>-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (<i>R</i><sup>2</sup>) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers. |
| format | Article |
| id | doaj-art-e26d15d0ad6b4193a6dadd3d423c12ae |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-e26d15d0ad6b4193a6dadd3d423c12ae2025-01-10T13:20:17ZengMDPI AGRemote Sensing2072-42922025-01-0117111910.3390/rs17010119Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural RiverManqi Wang0Caili Zhou1Jiaqi Shi2Fei Lin3Yucheng Li4Yimin Hu5Xuesheng Zhang6School of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaNanjing Institute of Environmental Sciences of the Ministry of Ecology and Environment, Nanjing 210042, ChinaInstitute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaInstitute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaThe continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH<sub>3</sub>-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (<i>R</i><sup>2</sup>) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers.https://www.mdpi.com/2072-4292/17/1/119UAV hyperspectral imagesremote sensingenvironmental monitoringmachine learning algorithmwater quality parametersmall rural river |
| spellingShingle | Manqi Wang Caili Zhou Jiaqi Shi Fei Lin Yucheng Li Yimin Hu Xuesheng Zhang Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River Remote Sensing UAV hyperspectral images remote sensing environmental monitoring machine learning algorithm water quality parameter small rural river |
| title | Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River |
| title_full | Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River |
| title_fullStr | Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River |
| title_full_unstemmed | Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River |
| title_short | Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River |
| title_sort | inversion of water quality parameters from uav hyperspectral data based on intelligent algorithm optimized backpropagation neural networks of a small rural river |
| topic | UAV hyperspectral images remote sensing environmental monitoring machine learning algorithm water quality parameter small rural river |
| url | https://www.mdpi.com/2072-4292/17/1/119 |
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