MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||7|
|Section||Parallel Session I: Water Resources System|
|Published online||07 December 2018|
Mid-long term runoff forecasting model based on RS-RVM
1 Nanjing Hydraulic Research Institute, State Key Lab of Hydrology-Water Resources and Hydraulic Engineering, 210029, Nanjing city, China
2 Hohai University, College of Hydrology and Water Resources, 210029, Nanjing city, China
3 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing city, 100101, China
a Corresponding author: Jian Hu: firstname.lastname@example.org
In view of the two key problems in hydrological mid-long term runoff forecasting-the selection of key forecasting factors and the construction of forecasting models, an analysis is made on, taking Danjiangkou Reservoir as an example, the basis of preliminarily identifying the sea-air physical factors such as atmospheric circulation, sea surface temperature and Southern Oscillation, et al. The rough set theory is used to establish the data decision table and reduce the factors, and the relevance vector machine method is adopted to establish the mid-long term runoff forecasting model based on reduced factor set. Meanwhile, this paper simulates and predicts the amount of runoff of the reservoir in September and October during the autumn floods from 1952 to 2008, and makes comparison with the model adopting support vector machine. The result shows that the relevance vector machine has better robustness and generalization performance. According to the standard of 20% annual variation, the simulation accuracy of September and October reaches 93.9% and 95.9%, respectively, and the accuracy of the trial forecasting is all up to standard. Moreover, this model better reflects the characteristics of ample flow period and low water period of the forecasting years.
© The Authors, published by EDP Sciences, 2018
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