Abstract
The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium- and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts.
Highlights
Runoff forecasting, especially medium- and long-term runoff forecasting, plays an important role in the comprehensive development, utilization, scientific management and optimization of water resources [1,2,3,4]
The main purpose of this paper is to build an adaptive data driven runoff forecast [36,37] model, by using the normalized mutual information method to automatically select predictors, and use the kernel principal component analysis (KPCA) method to extract features from the selected factors; based on the above, a cyclic neural network model is constructed for runoff prediction
In order to verify its validity, the model proposed in this paper attempts to forecast the annual runoff of Jinping I-Stage hydropower station in Yalong River [42] basin, Sichuan province, China
Summary
Runoff forecasting, especially medium- and long-term runoff forecasting, plays an important role in the comprehensive development, utilization, scientific management and optimization of water resources [1,2,3,4]. It is necessary to accurately predict the time and size of peak flow before a flood event [5]. Accurate prediction of medium- and long-term runoff is an important prerequisite for guiding the comprehensive development and utilization of water resources, scientific management, and optimal dispatch. A data-driven model refers to the optimal mathematical relationship between a forecast object (such as annual average runoff) and a predictor (such as the circulation index) based on historical data, regardless of the physical mechanism of the hydrological process
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