Random Forest Model Has the Potential for Runoff Simulation and Attribution
Abstract
:1. Introduction
2. Study Area, Data Sets, and Methods
2.1. Study Area
2.2. Data Sets
2.3. Methods
2.3.1. Trend and Breakpoint Analysis
2.3.2. Elasticity-Based Methods
2.3.3. Assessment Based on Random Forest Model
2.3.4. Assessment Based on Double-Mass Curve Method
3. Results
3.1. Trends of Climatic, Hydrological, and Anthropogenic Variables
3.2. Performance of Different Models
3.3. Quantifying the Runoff Change Response to Climate Change and Human Activities in the Target Basins
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Slope | ||
---|---|---|
Daomaguan | Zijinguan | |
Precipitation | −0.003 | 0.003 |
Air temperature | 0.011 * | 0.006 |
Actual evapotranspiration | 0.001 | <0.001 |
NDVI | 0.579 * | 0.359 |
Runoff | −0.005 * | −0.008 * |
Data Period | Method | Before and after Breakpointperiods | ΔP (mm) | ΔET0 (mm) | ΔR (mm) | Contribution to Runoff Changes (%) | |
---|---|---|---|---|---|---|---|
Human | Climate | ||||||
Daomaguan Basin | Double-mass curve method | 1982–1998 1999–2014 | −43.64 | 82.41 | −29.76 | 69% | 31% |
Elasticity-based method | 56% | 44% | |||||
Random Forest method | 66% | 34% | |||||
Zijinguan Basin | Double-mass curve method | 1982–2000 2001–2014 | 0.71 | 23.58 | −50.18 | 94% | 6% |
Elasticity-based method | 91% | 9% | |||||
Random Forest method | 94% | 6% |
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Liu, X.; Zhang, X.; Kong, X.; Shen, Y.-J. Random Forest Model Has the Potential for Runoff Simulation and Attribution. Water 2022, 14, 2053. https://doi.org/10.3390/w14132053
Liu X, Zhang X, Kong X, Shen Y-J. Random Forest Model Has the Potential for Runoff Simulation and Attribution. Water. 2022; 14(13):2053. https://doi.org/10.3390/w14132053
Chicago/Turabian StyleLiu, Xia, Xiaolong Zhang, Xiaole Kong, and Yan-Jun Shen. 2022. "Random Forest Model Has the Potential for Runoff Simulation and Attribution" Water 14, no. 13: 2053. https://doi.org/10.3390/w14132053