Long-term Runoff Forecasting Models Based on the Teleconnection coupled with Machine Learning
- 1Insititute of Hydrology and Water Resources, Department of Hydraulic Engineering,Tsinghua University, Beijing, China (t-zhang18@mails.tsinghua.edu.cn)
- 2State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China
- 3State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, China
Runoff forecast with high precision is important for the efficient utilization of water resources and regional sustainable development, especially in the arid area. The monthly runoff of Changmabao (CMB) station has an upwards trend and an abrupt point in 1998. The impact factor analysis shows that it is highly correlated with the current precipitation and temperature in the wet season while the previous runoff and previous global land temperature in the dry season. Three models including the time-series decomposition model, the model based on teleconnection coupled with the support vector machine, and the model based on teleconnection coupled with the artificial neural network are used to predict the runoff of CMB station. An indicator β is constructed with the correlation coefficient (R) and mean relative deviation (rBias) to evaluate the model performance more conveniently and intuitively. The results suggest that the model based on teleconnection coupled with the support vector machine preforms best. This forecasting method could be applied to the management and dispatch of water resources in arid areas.
How to cite: Zhang, T., Wang, Z., and Zhang, Z.: Long-term Runoff Forecasting Models Based on the Teleconnection coupled with Machine Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1369, https://doi.org/10.5194/egusphere-egu2020-1369, 2020.
