Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning
Abstract
:1. Introduction
- (1)
- using UAV with multispectral camera to capture images and surveyed cross-section under shallow bathymetry conditions;
- (2)
- applying a ML algorithm to establish a water depth retrieval model for the rivers of Taiwan’s mountain area, which has yet been found in related studies;
- (3)
- encrypting the established model to Python-based program for further application, which can be used to simulate water depth for the shallow river or near-shore areas that are not easily measured under similar conditions as this study, where the water depth could be estimated by multispectral sensor mounted on the UAV.
2. Materials and Methods
2.1. Study Area
2.2. Bathymetry Investigation
2.3. UAV and Multispectral Camera
2.4. Data Processing
2.5. Development of Water Depth Retrieval Model
2.5.1. Gene-Expression Programming (GEP)
2.5.2. Simple Linear Regression (REG)
2.6. Model Accuracy Evaluation
3. Results
3.1. Simulation Results and Accuracy Evaluation
3.2. Error Evaluation
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Sub-ETs of GEP Bathymetry Retrieval Model
Appendix B. Python-Based Code for Shallow Water Depth Retrieval by UAV Imagery
- y = ((((C2+d[3])*min(d[3],d[0],d[0]))+pow(pow(d[0],4.0),(d[2]*d[3]))+((floor (d[3])+pow(10.0,d[1])+C1)/3.0))/3.0)
- y = min(y,exp(atan(((floor(min(d[0],d[2],d[0]))/exp((d[0]-d[2]-d[3]))/(d[0]*C4* d[0]*C3))+C3))))
- y = min(y,pow(exp((max(d[2],d[0],d[0],d[3])+((d[0]+d[3]+d[3]+d[3])/4.0)+d[0]+ d[2])),((pow(C5,3.0)+(d[0]*C6*d[2])+(d[3]+d[3]+d[3]+d[3])+(C7*d[2]*C5))/4.0)))
- y = min(y,((((d[0]+d[1]+C8+d[0])+(d[1]/d[2]/d[0]/d[2])+pow(d[0],5.0)+pow (d[0],d[3]))+(((d[0]+d[2]+d[2]+d[3])/4.0)-d[0]-d[3]-d[3])+((d[1]+d[3]+d[2]+d[0]) /4.0)+(d[3]*d[2]*d[0]*d[0]))/4.0))
- y = min(y,max((1.0-(gepMod(C15,d[2])*C9*C10*d[0])),(((C10+C11+C12)/3.0)), (max(C12,C9,d[1],C13)*sin(d[3])*log(C14))))
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Items | 6 April 2016 | 10 April 2017 | 2 May 2017 |
---|---|---|---|
Images | 772 | 1076 | 856 |
Median of Key-points per Image | 20,000 | 21,111 | 53,203 |
Median of Matching Points per Image | 8870.85 | 5889.67 | 12,485.60 |
Ground Control Points (GCP) | 20 | 19 | 6 |
The Mean RMS Error of GCP in X-axis | 0.009 m | 0.006 m | 0.003 m |
The Mean RMS Error of GCP in Y-axis | 0.007 m | 0.004 m | 0.004 m |
The Mean RMS Error of GCP in Z-axis | 0.013 m | 0.008 m | 0.004 m |
Number of 3D Densified Points | 97,694,554 | 125,818,685 | 400,257,260 |
Average Density (per m3) | 37.37 | 64.61 | 776.11 |
Index | Max | Min | Average | Standard Deviation |
---|---|---|---|---|
Green | 0.094 | 0.038 | 0.071 | 0.008 |
NIR | 0.079 | 0.044 | 0.065 | 0.008 |
NDVI | 0.148 | −0.323 | −0.163 | 0.071 |
NDWI | 0.264 | −0.290 | 0.044 | 0.093 |
Variable | Average Values of Variables | p-Value | Significant Difference | ||
---|---|---|---|---|---|
Raw Dataset | Training | Testing | |||
Green | 0.071 | 0.070 | 0.072 | 0.411 | - |
NIR | 0.065 | 0.065 | 0.064 | 0.446 | - |
NDVI | −0.163 | −0.162 | −0.172 | 0.450 | - |
NDWI | 0.044 | 0.041 | 0.063 | 0.244 | - |
Water Depth (m) | 0.646 | 0.633 | 0.701 | 0.386 | - |
Model | R2 | MAE (m) | ME (m) | RMSE (m) | ||||
---|---|---|---|---|---|---|---|---|
Training | Testing | Training | Testing | Training | Testing | Training | Testing | |
GEP | 0.632 | 0.801 | 0.188 | 0.154 | 0.003 | 0.012 | 0.242 | 0.195 |
REG | 0.569 | 0.729 | 0.211 | 0.184 | <0.001 | −0.008 | 0.262 | 0.225 |
Model | Observed Bathymetry (m) | |||
---|---|---|---|---|
<0.4 | 0.4–0.8 | 0.8–1.48 | 0–1.48 | |
MAE (m) | ||||
GEP | 0.194 | 0.110 | 0.163 | 0.154 |
REG | 0.220 | 0.112 | 0.221 | 0.184 |
Study | Tool | Factor | Value (m) | Range (m) | |
---|---|---|---|---|---|
Method | Remote/Contact 1 | ||||
Kasvi et al. [16] | ADCP 2 | C | MAE | 0.030–0.070 (avg. 0.053) | 0.20–1.50 |
Kasvi et al. [16] | REG | R | MAE | 0.050–0.170 (avg. 0.112) | 0.00–1.50 |
This study | ML (GEP) | R | MAE | 0.154 | 0.01–1.53 |
This study | REG | R | MAE | 0.184 | 0.01–1.53 |
Kasvi et al. [16] | SfM | R | MAE | 0.180–2.980 (avg. 0.740) | 0.00–1.50 |
This study | REG | R | ME | −0.008 | 0.01–1.53 |
This study | ML (GEP) | R | ME | 0.012 | 0.01–1.53 |
Kasvi et al. [16] | ADCP 2 | C | ME | −0.030–0.000 (avg. −0.015) | 0.20–1.50 |
Kasvi et al. [16] | REG | R | ME | −0.170–0.020 (avg. −0.087) | 0.00–1.50 |
Jérôme et al. [21] | REG | R | ME | 0.130 | 0.09–1.01 |
Mandlburger et al. [39] | ML (DL) | R | ME | 0.150 | 0.00–12.00 |
Kasvi et al. [16] | SfM | R | ME | −0.180–3.200 (avg. 0.357) | 0.00–1.50 |
Sagawa et al. [40] | ML (RF) | R | ME | 0.250–1.370 (avg. 1.008) | 0.00–5.00 |
This study | ML (GEP) | R | RMSE | 0.195 | 0.01–1.53 |
This study | REG | R | RMSE | 0.225 | 0.01–1.53 |
Lee et al. [23] | ML (NN) | R | RMSE | 0.310–0.400 (avg. 0.358) | 1.50–9.00 |
Lee et al. [23] | MBVA | R | RMSE | 0.440 | 1.50–9.00 |
Sandidge and Holyer [41] | ML (NN) | R | RMSE | 0.480 | 0.00–6.00 |
Lee et al. [23] | ML (NN) | R | RMSE | 0.510–0.520 (avg. 0.515) | 1.00–11.00 |
Lee et al. [23] | MBVA | R | RMSE | 0.540 | 1.00–11.00 |
Lee et al. [23] | TBRA | R | RMSE | 1.020 | 1.50–9.00 |
Lee et al. [23] | TBRA | R | RMSE | 1.250 | 1.00–11.00 |
Hernandez and Armstrong [15] | REG | R | RMSE | 1.260 | 1.00–10.00 |
Su et al. [34] | REG | R | RMSE | 1.340 | 0.00–5.00 |
Sagawa et al. [40] | ML (RF) | R | RMSE | 0.830–1.910 (avg. 1.634) | 0.00–5.00 |
Su et al. [34] | ML (LM) | R | RMSE | 2.070 | 0.00–5.00 |
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Lee, C.-H.; Liu, L.-W.; Wang, Y.-M.; Leu, J.-M.; Chen, C.-L. Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning. Remote Sens. 2022, 14, 3343. https://doi.org/10.3390/rs14143343
Lee C-H, Liu L-W, Wang Y-M, Leu J-M, Chen C-L. Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning. Remote Sensing. 2022; 14(14):3343. https://doi.org/10.3390/rs14143343
Chicago/Turabian StyleLee, Chih-Hung, Li-Wei Liu, Yu-Min Wang, Jan-Mou Leu, and Chung-Ling Chen. 2022. "Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning" Remote Sensing 14, no. 14: 3343. https://doi.org/10.3390/rs14143343
APA StyleLee, C.-H., Liu, L.-W., Wang, Y.-M., Leu, J.-M., & Chen, C.-L. (2022). Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning. Remote Sensing, 14(14), 3343. https://doi.org/10.3390/rs14143343