Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning

Abstract

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 (R2) 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 (<0.4 m) and deep waters (>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.

Evaluating gradient descent variations for artificial neural network bathymetry modeling and sensitivity analysis

“…Bathymetry and multispectrum investigation area of this study, after Ref. 17. The pink and blue color points were the investigations marks in 2016 and 2017, respectively.…”

“…1, after Ref. 17) during the dry season of 2016 to 2017. For the ground investigation, a total of 171 bathymetry data points were surveyed, ranging from 0.01 to 1.53 m. These measurements were taken using the Trimble R6-II RTK GPS, which boasts a vertical accuracy of 15 mm + 0.5 ppm RMS and a horizontal accuracy of 8 mm + 0.5 ppm RMS.…”

“…The detailed data observation and processing information can be obtained in Ref. 17. Briefly, in the study, the number of aerial images taken in a single UAV flight mission ranged from 772 to 1076, on April 6, 2016, April 10, 2017, and May 2, 2017, respectively.…”

Riverine Bathymetry Survey Using Unmanned Aerial Vehicle-Based Multispectral Image

“…하천의 수심은 하천 생태계의 물리적 구조와 역학을 분석 하는 데 필수적이다 (Dekker et al, 2011;Lee et al, 2022;Mallick et al, 2014). 특히, 수심 데이터는 하천의 흐름과 퇴적물 이송을 모델링하는 필수 요소이며, 이는 효과적인 수 자원 관리와 홍수 예측에 중요하다 (McKean et al, 2014;Naganna et al, 2017) (Al Najar et al, 2022;Fonstad and Marcus, 2005;Legleiter et al, 2019) 는 연구들이 진행되고 있다 (Gwon et al, 2023a;Gwon et al, 2023b;Gwon et al, 2023c;Kwon at al., 2023).…”

Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records

“…In this context, despite the availability of various technologies that enable bathymetry measurements with sufficient accuracy to map river cross-sections, practitioners face the challenging task of transporting heavy, expensive equipment into the field, where they often spend hours or even days collecting data [52,53]. This logistical complexity, as noted in several studies [54,55], has generated a paucity of research in this area, which in turn has limited the scope of studies or restricted them to locations of specific interest.…”