Author(s): Wentong Hu; Wenquan Gu; Zhenhua Wang; Jie Liu; He Wang; Dongguo Shao; Yanping Hu
Linked Author(s): He Wang, Jie Liu, Wentong Hu, Zhenhua Wang
Keywords: Total nitrogen retrieval; Machine learning; Unmanned aerial vehicles; Spatiotemporal distribution
Abstract: Inland small waterbodies are important water sources for human, and are also the cornerstone for maintaining ecological environment stability. It is of great significance to carry out accurate, fast, and green efficient monitoring of inland water quality. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, and unmanned aerial vehicles (UAV) low-altitude remote sensing provides opportunities for water quality retrieval in these regions. This study proposed a machine learning based water quality retrieval framework that integrated spatiotemporal information, feature engineering and intelligent optimization algorithms to retrieve total nitrogen (TN) in inland small waterbodies. The results showed that the RMSE of the training, validation, and test sets was 0.036-0.21 mg/L, R2 was 0.918-0.998, RPD was 3.483-20.052, indicating that the established framework has high accuracy. Furthermore, the established framework was applied to multispectral images obtained from six UAV flights, and it was found that the predicted quartiles and mean values of TN concentration in each research area were close to the measured, with an error of less than 20%. Therefore, it is considered that the established model framework has high transferability. Although there are still some uncertainties in the water quality retrieval model constructed in this study, such as significant differences in the retrieval results of various models in part of the waterbodies, the overall spatial distribution is consistent. This study has achieved a transformation of water environment monitoring from point to surface, which can provide reference for intelligent management of small waterbodies.
Year: 2024