Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning
Abstract
1. Introduction
2. Methods
2.1. Study Watershed
2.2. SWAT Input Data and Overview
2.3. Machine Learning for Spatiotemporal Alpha Factor Estimation in SWAT: A TPOT-Based Approach
2.4. Web-Based Program for Spatiotemporal Alpha Factor Estimation in SWAT Using Automated Machine Learning (TPOT)
2.5. Assessment of Recession and Baseflow Estimation in Case 1 and Case 2
3. Results and Discussion
3.1. Development and Evaluation of a Web-Based Program for Spatiotemporal Alpha Factor Estimation in SWAT Using Automated Machine Learning (TPOT)
3.2. Comparison of Recessions Estimation in Case 1 and Case 2
3.3. Comparison of Baseflow Estimation in Case 1 and Case 2
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year | Annual Precipitation (mm) | Monthly Precipitation (mm) | Annual Temperature (°C) | ||
---|---|---|---|---|---|
Max. | Min. | Max. | Min. | ||
2010 | 1419.7 | 376.4 | 16.4 | 24.7 | 1.3 |
2011 | 1943.4 | 587.3 | 4.0 | 23.4 | 2.4 |
2012 | 1409.5 | 463.6 | 2.5 | 24.2 | 2.3 |
2013 | 1120.2 | 218.7 | 19.9 | 25.9 | 2.0 |
2014 | 1117.7 | 240.9 | 6.5 | 24.8 | 3.1 |
2015 | 822.7 | 145.6 | 27.0 | 25.4 | 2.7 |
2016 | 1228.4 | 367.9 | 11.6 | 26.4 | 2.6 |
2017 | 1127.5 | 434.5 | 11.6 | 25.3 | 2.3 |
Data Type | Name | Source |
---|---|---|
Spatial data | Digital Elevation Model (DEM) | National Geographic Information Institute |
Land use | Korea Ministry of Environment | |
Soil type | Korea Rural Development Administration | |
Meteorological data | Precipitation, wind speed, maximum and minimum temperature, relative humidity, and solar radiation (daily timeseries, 2010–2017) | Korea Meteorological Administration |
Hydrological data | Streamflow (daily timeseries, 2010–2017) | Water resource Management Information System |
Study Watershed | R2 | NSE | IOA | MAPE (%) | PBIAS (%) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | |
Yongchon | 0.58 | 0.58 | 0.57 | 0.56 | 0.83 | 0.83 | 66.17 | 65.87 | 21.54 | 21.48 |
Dugye | 0.78 | 0.78 | 0.73 | 0.74 | 0.93 | 0.93 | 65.47 | 65.34 | 35.26 | 35.28 |
Munam | 0.56 | 0.57 | 0.56 | 0.56 | 0.84 | 0.83 | 68.97 | 67.15 | 9.51 | 9.02 |
Inchang | 0.57 | 0.58 | 0.56 | 0.56 | 0.81 | 0.83 | 72.51 | 70.13 | 9.66 | 9.14 |
Gasuwon | 0.63 | 0.63 | 0.62 | 0.63 | 0.88 | 0.88 | 57.71 | 56.98 | −8.36 | −7.67 |
Boksu | 0.66 | 0.67 | 0.62 | 0.63 | 0.90 | 0.91 | 68.63 | 66.91 | −8.82 | −8.03 |
Hanbat | 0.57 | 0.58 | 0.58 | 0.57 | 0.86 | 0.86 | 63.02 | 61.53 | 12.92 | 12.14 |
Mannyeon | 0.66 | 0.66 | 0.64 | 0.65 | 0.86 | 0.85 | 65.16 | 64.75 | 7.25 | 7.13 |
Daedoek | 0.64 | 0.64 | 0.57 | 0.57 | 0.83 | 0.64 | 76.63 | 75.02 | 43.98 | 41.72 |
Wonchon | 0.54 | 0.54 | 0.53 | 0.54 | 0.84 | 0.83 | 67.32 | 66.81 | −0.63 | 1.44 |
Study Watershed | R2 | NSE | IOA | MAPE (%) | PBIAS (%) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | |
Yongchon | 0.78 | 0.66 | 0.44 | 0.57 | 0.79 | 0.83 | 46.66 | 37.84 | 40.45 | 22.12 |
Dugye | 0.33 | 0.62 | 0.17 | 0.60 | 0.77 | 0.86 | 64.93 | 50.23 | −20.24 | −11.46 |
Munam | 0.71 | 0.73 | 0.49 | 0.54 | 0.78 | 0.78 | 33.03 | 30.26 | 35.94 | 25.69 |
Inchang | 0.85 | 0.75 | 0.61 | 0.64 | 0.81 | 0.85 | 39.45 | 20.17 | 13.73 | 9.19 |
Gasuwon | 0.68 | 0.76 | 0.62 | 0.72 | 0.88 | 0.90 | 43.46 | 35.15 | −23.04 | −12.50 |
Boksu | 0.63 | 0.74 | 0.53 | 0.62 | 0.76 | 0.83 | 35.87 | 33.90 | 32.23 | 17.40 |
Hanbat | 0.76 | 0.72 | 0.62 | 0.65 | 0.84 | 0.87 | 34.32 | 30.67 | 24.57 | 19.54 |
Mannyeon | 0.56 | 0.78 | 0.51 | 0.66 | 0.78 | 0.86 | 45.21 | 32.18 | −11.48 | −8.93 |
Daedoek | 0.51 | 0.78 | 0.42 | 0.51 | 0.72 | 0.76 | 51.20 | 40.16 | −54.28 | −25.08 |
Wonchon | 0.78 | 0.80 | 0.68 | 0.74 | 0.86 | 0.90 | 35.56 | 33.20 | 12.31 | 16.70 |
Study Watershed | R2 | NSE | IOA | MAPE (%) | PBIAS (%) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | Case 1 | Case 2 | |
Yongchon | 0.63 | 0.61 | 0.46 | 0.57 | 0.81 | 0.86 | 58.09 | 54.21 | 46.58 | 22.72 |
Dugye | 0.50 | 0.56 | 0.40 | 0.52 | 0.82 | 0.85 | 57.94 | 50.58 | 30.48 | 18.02 |
Munam | 0.64 | 0.66 | 0.58 | 0.64 | 0.82 | 0.86 | 71.57 | 61.62 | 31.63 | 21.19 |
Inchang | 0.65 | 0.67 | 0.63 | 0.64 | 0.88 | 0.89 | 65.14 | 64.27 | 26.92 | 30.28 |
Gasuwon | 0.67 | 0.67 | 0.51 | 0.52 | 0.89 | 0.88 | 57.51 | 45.65 | 14.60 | −5.46 |
Boksu | 0.60 | 0.63 | 0.59 | 0.62 | 0.86 | 0.87 | 61.96 | 56.28 | 21.85 | 14.51 |
Hanbat | 0.56 | 0.58 | 0.48 | 0.53 | 0.84 | 0.86 | 59.95 | 40.32 | 33.96 | 26.14 |
Mannyeon | 0.61 | 0.62 | 058 | 0.60 | 0.87 | 0.88 | 59.95 | 51.22 | 15.55 | −1.05 |
Daedoek | 0.55 | 0.72 | 0.48 | 0.51 | 0.83 | 0.90 | 60.59 | 27.77 | 31.59 | −27.41 |
Wonchon | 0.66 | 0.76 | 0.65 | 0.74 | 0.89 | 0.93 | 58.65 | 38.73 | 16.57 | −6.88 |
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Lee, J.; Han, J.; Engel, B.; Lim, K.J. Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning. Environments 2025, 12, 94. https://doi.org/10.3390/environments12030094
Lee J, Han J, Engel B, Lim KJ. Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning. Environments. 2025; 12(3):94. https://doi.org/10.3390/environments12030094
Chicago/Turabian StyleLee, Jimin, Jeongho Han, Bernard Engel, and Kyoung Jae Lim. 2025. "Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning" Environments 12, no. 3: 94. https://doi.org/10.3390/environments12030094
APA StyleLee, J., Han, J., Engel, B., & Lim, K. J. (2025). Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning. Environments, 12(3), 94. https://doi.org/10.3390/environments12030094