Combined Forecasting of Streamflow Based on Cross Entropy

Abstract

Abstract:In this study, we developed a model of combined streamflow forecasting based on cross entropy to solve the problems of streamflow complexity and random hydrological processes. First, we analyzed the streamflow data obtained from Wudaogou station on the Huifa River, which is the second tributary of the Songhua River, and found that the streamflow was characterized by fluctuations and periodicity, and it was closely related to rainfall. The proposed method involves selecting similar years based on the gray correlation degree. The forecasting results obtained by the time series model (autoregressive integrated moving average), improved grey forecasting model, and artificial neural network model (a radial basis function) were used as a single forecasting model, and from the viewpoint of the probability density, the method for determining weights was improved by using the cross entropy model. The numerical results showed that compared with the single forecasting model, the combined forecasting model improved the stability of the forecasting model, and the prediction accuracy was better than that of conventional combined forecasting models.

Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China

“…The input of autoregressive model is only previous monthly streamflow data, which may influence the forecast accuracy of the autoregressive model in the flood season. Therefore, adding precipitation as a predictor, selecting one or more models with high accuracy in the flood season, and using the entropy spectrum model and its combination (such as combined streamflow forecasting based on cross entropy [36]) to forecast can be used as the next research direction.…”

Research of Teaching Innovation on the Course of Water Resources Planning and Utilization

“…Due to the complexity, diversity and cross-cutting trait of water resources issues, in the teaching process we try to encourage students to think more and search for water-related problems that they can attempt to resolve. We have given guidance to more than 10 students on the knowledge of water and the environment, water and ecology, for their application for innovation and entrepreneurship projects (Men, Long, & Zhang, 2016;Zhan, Men, Wu et al, 2015;Zhao, Men, Wang et al, 2014;Men, Long, Zhao et al, 2015;Li & Men, 2016).…”

BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks

“…For time series model, ignoring the diffusion mechanism of public opinion, the diffusion model of public opinion is constructed only based on time series data. Auto-regressive integrated moving average (ARIMA) [13], artificial neural network [14] and support vector machines [15,16] are the frequently used models for time series data. Due to effectiveness of time series model for public opinion prediction, the time series model is applied for the prediction of the numbers of posts, blogs or micro-blogs.…”