Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Machine Learning
2.2.1. Data Preparation
2.2.2. Prophet Algorithm
2.2.3. Performance Analysis
2.3. Terrestrial LiDAR and 3D River Channel Modeling
2.4. Upstream Volume Calculations
3. Results
3.1. Machine Learning
3.2. LiDAR 3D Models and Upstream Volume Calculations
4. Discussion
Methodology Applications
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Site Name | Data Available | Temporal Resolution | Variables | Number of Records | Closest Weather Station |
---|---|---|---|---|---|
MS1414 | 3/31/2022–10/31/2022 | 13 min | Water temperature (°C), turbidity (NTUs), air temperature (°C), precipitation (mm), relative humidity (%) | 363 | Weather station 1 |
MS3359 | 4/16/2022–8/8/2022 | 14 min | Dissolved oxygen (% saturation), turbidity (NTUs), conductivity (µS/m), water temperature (°C), air temperature (°C), precipitation (mm), relative humidity (%) | 573 | Weather station 3 |
NHC296 | 9/14/2021–11/18/2022 | 12 min | Water temperature (°C), air temperature (°C), precipitation (mm), relative humidity (%) | 2598 | Weather station 2 |
NHC1278 | 4/11/2022–11/12/2022 | 12 min | Air temperature (°C), precipitation (mm), relative humidity (%) | 1473 | Weather station 3 |
NHC1787 | 4/11/2022–12/7/2022 | 11 min | Dissolved oxygen (% saturation), turbidity (NTUs), conductivity (µS/m), water temperature (°C), air temperature (°C), precipitation (mm), relative humidity (%) | 1828 | Weather station 3 |
Station Name | RMSE | MAE | R2 |
---|---|---|---|
MS1414 | 0.446 | 0.318 | 0.986 |
MS3359 | 0.535 | 0.433 | 0.944 |
NHC296 | 1.002 | 0.710 | 0.927 |
NHC1278 | 0.813 | 0.485 | 0.909 |
NHC1787 | 0.729 | 0.442 | 0.964 |
Site Name | Point Spacing (m) | Point Density (m2) | DEM Resolution (m) | Mean Slope (%) |
---|---|---|---|---|
MS292 | 0.006 | 27,778 | 0.03 | 36.5 |
MS1414 | 0.004 | 62,500 | 0.03 | 27.2 |
MS3359 | 0.01 | 10,000 | 0.028 | 33.6 |
NHC296 | 0.008 | 15,625 | 0.024 | 27.6 |
NHC1278 | 0.009 | 12,346 | 0.032 | 27.0 |
NHC1787 | 0.009 | 12,346 | 0.024 | 26.5 |
NHC1876 | 0.008 | 15,625 | 0.024 | 29.9 |
BG1128 | 0.006 | 27,778 | 0.018 | 27.6 |
Site Information | Base Flow | High Water Level | Increase in Volume | ||||||
---|---|---|---|---|---|---|---|---|---|
Name | Catchment Area (m2) | Water Level Elevation (m) | Upstream Volume (m3) | Two Foot Cross Section Volume (m3) | Water Level Elevation (m) | Upstream Volume (m3) | Two Foot Cross Section Volume (m3) | Upstream Volume (m3) | Two Foot Cross Section Volume (m3) |
MS292 | 1333.4 | 224.2 | 5.26 | 0.095 | 224.83 | 44.85 | 1.24 | 39.59 | 1.145 |
MS1414 | 390.5 | 208.1 | 21.58 | 0.33 | 208.87 | 200.30 | 2.45 | 178.72 | 2.12 |
MS3359 | 1338.9 | 187.3 | 345.33 | 0.13 | 188.35 | 928.99 | 2.64 | 583.66 | 2.51 |
NHC296 | 3628.2 | 228.5 | 13.39 | 1.03 | 229.5 | 52.03 | 3.24 | 38.64 | 2.21 |
NHC1278 | 956.4 | 205.5 | 61.22 | 0.13 | 205.93 | 135.63 | 1.37 | 74.41 | 1.24 |
NHC1787 | 822.8 | 194.8 | 25.97 | 0.38 | 195.86 | 352.82 | 4.68 | 326.85 | 4.3 |
NHC1876 | 412.4 | 194 | 7.69 | 0.47 | 194.9 | 107.41 | 2.91 | 99.72 | 2.44 |
BG1128 | 685.3 | 205 | 7.80 | 0.02 | 205.4 | 48.55 | 0.22 | 40.75 | 0.2 |
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Bolick, M.M.; Post, C.J.; Naser, M.Z.; Forghanparast, F.; Mikhailova, E.A. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling. Water 2023, 15, 2581. https://doi.org/10.3390/w15142581
Bolick MM, Post CJ, Naser MZ, Forghanparast F, Mikhailova EA. Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling. Water. 2023; 15(14):2581. https://doi.org/10.3390/w15142581
Chicago/Turabian StyleBolick, Madeleine M., Christopher J. Post, M. Z. Naser, Farhang Forghanparast, and Elena A. Mikhailova. 2023. "Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling" Water 15, no. 14: 2581. https://doi.org/10.3390/w15142581
APA StyleBolick, M. M., Post, C. J., Naser, M. Z., Forghanparast, F., & Mikhailova, E. A. (2023). Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling. Water, 15(14), 2581. https://doi.org/10.3390/w15142581