Issue |
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
|
|
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Article Number | 02028 | |
Number of page(s) | 4 | |
Section | Parallel Session I: Water Resources System | |
DOI | https://doi.org/10.1051/matecconf/201824602028 | |
Published online | 07 December 2018 |
Long-term runoff prediction for reservoir based on Mahalanobis distance discrimination
1 College of Hydrology and Water Resources, Hohai University, Nanjing city, 210098, China
2 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing city, 210029, 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: LIU Yong: yongliu@nhri.cn
An accurate and timely forecast of medium and long-term runoff forecast is of great significance to reservoir safety and water resources scheduling. In order to improve the long-term runoff forecast accuracy of the reservoir, a long-term runoff forecasting model was constructed based on the principle of Mahalanobis distance discrimination analysis. The data sequence from 1952 to 2008 of Danjiangkou reservoir was selected, the correlation coefficient method and AIC criterion were used to sift out the highly correlated and independent factors, a long-term runoff forecasting model was constructed based on the principle of Mahalanobis distance discrimination analysis. The result showed that under the permutation error of 10%,the pass rate during the simulation period was 93.9%, and the pass rate during the inspection period was 87.5%. The research results serve as a reference for the operation of Danjiangkou reservoir.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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