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Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning
Content Provider | MDPI |
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Author | Lee, Chih-Hung Liu, Li-Wei Wang, Yu-Min Leu, Jan-Mou Chen, Chung-Ling |
Copyright Year | 2022 |
Description | The river cross-section elevation data are an essential parameter for river engineering. However, due to the difficulty of mountainous river cross-section surveys, the existing bathymetry investigation techniques cannot be easily applied in a narrow and shallow field. Therefore, this study aimed to establish a model suitable for mountainous river areas utilizing an unmanned aerial vehicle (UAV) equipped with a multispectral camera and machine learning-based gene-expression programming (GEP) algorithm. The obtained images were combined with a total of 171 water depth measurements (0.01–1.53 m) for bathymetry modeling. The results show that the coefficient of determination $(R^{2}$) of GEP is 0.801, the mean absolute error (MAE) is 0.154 m, and root mean square error (RMSE) is 0.195 m. The model performance of GEP model has increased by 16.3% in MAE, compared to conventional simple linear regression (REG) algorithm, and also has a lower bathymetry retrieval error both in shallow (0.8 m). The GEP bathymetry retrieval model has a considerable degree of accuracy and could be applied to shallow rivers or near-shore areas under similar conditions of this study. |
Starting Page | 3343 |
e-ISSN | 20724292 |
DOI | 10.3390/rs14143343 |
Journal | Remote Sensing |
Issue Number | 14 |
Volume Number | 14 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-07-11 |
Access Restriction | Open |
Subject Keyword | Remote Sensing River Survey Mountainous River Cross-section Gene-expression Programming (gep) Chishan River Basin Multispectral Camera Unmanned Aerial Vehicle (uav) |
Content Type | Text |
Resource Type | Article |