دورية أكاديمية
Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms
| العنوان: | Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms |
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| المؤلفون: | Yujie Chen, Ke Yao, Beibei Zhu, Zihao Gao, Jie Xu, Yucheng Li, Yimin Hu, Fei Lin, Xuesheng Zhang |
| المصدر: | Water, Vol 16, Iss 4, p 553 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Hydraulic engineering LCC:Water supply for domestic and industrial purposes |
| مصطلحات موضوعية: | UAV multispectral data, Catboost Regression, total nitrogen and total phosphorus, turbidity, medium/small-sized water bodies, small sample size, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500 |
| الوصف: | Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2073-4441 |
| Relation: | https://www.mdpi.com/2073-4441/16/4/553; https://doaj.org/toc/2073-4441 |
| DOI: | 10.3390/w16040553 |
| URL الوصول: | https://doaj.org/article/3925f6e0ba794d8eb3292574b1843afa |
| رقم الأكسشن: | edsdoj.3925f6e0ba794d8eb3292574b1843afa |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20734441 |
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| DOI: | 10.3390/w16040553 |