Abstract
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.
Highlights
2 Research methods2.1 Single correlation coefficientCorrelation coefficients are often used in hydrological mid and long-term predictions to investigate whether linear correlation between predictors and forecast objects is used as a basis for factor selection [3]
The selection of the number of factors p has a great influence on the prediction accuracy and stability of the model
The Mahalanobis distance was proposed by the Indian statistician Mahalanobis
Summary
Correlation coefficients are often used in hydrological mid and long-term predictions to investigate whether linear correlation between predictors and forecast objects is used as a basis for factor selection [3]. The formula for the single correlation coefficient is: r= ∑ Where Xi and Yi are the series of factors and forecast objects respectively; X and Y are their mean values; n is the length of the sequence, r is a single correlation. The tα can be found from the t-distribution table after α is determined. When t>tα, it is considered that the two are linearly correlated under this reliability, otherwise it is considered to be linearly uncorrelated
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