North China University of Water Conservancy and Electric Power

About: North China University of Water Conservancy and Electric Power is a education organization based out in Zhengzhou, China. It is known for research contribution in the topics: Excited state & Hydrogen bond. The organization has 4241 authors who have published 3630 publications receiving 23965 citations. The organization is also known as: North China University of Water Resources and Electric Power & North China Institute of Water Resources and Hydropower.

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Abstract: Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

High-Concentration Aqueous Dispersions of MoS2

Abstract: Molybdenum disulfide (MoS2) nanosheets have been attracting increasing research interests due to their unique material properties. However, the lack of a reliable large-scale production method impedes their practical applications. Here a facile, efficient, and scalable method for the fabrication of high-concentration aqueous dispersion of MoS2 nanosheets using combined grinding and sonication is reported. The 26.7 +/- 0.7 mg/mL concentration achieved is the highest concentration in an aqueous solution reported up to now. Grinding generates pure shear forces to detach the MoS2 layers from the bulk materials. Subsequent sonication further breaks larger crystallites into smaller crystallites, which promotes the dispersion of MoS2 nanosheets in ethanol/water solutions. The exfoliation process establishes a new paradigm in the top-down fabrication of 2D nanosheets in aqueous solution. In the meantime, MoS2-based sensing film produced using this approach has successfully demonstrated the feasibility of a low-cost and efficient NH3 gas sensor using inkjet printing as a viable method.

Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD

Abstract: Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation ( R ) and Nash–Sutcliffe efficiency (NSE). The PSO–SVM–EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station.