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 NashSutcliffe 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.

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The influence of a molten liquid polymer layer on the crystallization of the beneath semicrystalline polymer has been seldom considered. In the study, the nucleation and growth of spherulites for the beneath polylactide (PLA) layer in poly(ethylene oxide)/polylactide (PEO/PLA) double-layer films during isothermal crystallization at various temperatures above the melting point of PEO have been investigated by using polarized optical microscopy, with the particular results compared with that for neat PLA and PLA/PEO blend films. It is interesting to find that the top covering molten PEO layer can greatly accelerate the spherulitic growth rate (G) of the beneath PLA layer. Another significant result is that the temperature for the measurable nucleation and spherulitic growth of PLA in the double-layer films can be eventually pushed down close to the glass transition temperature of neat PLA. The changes of glass transition temperature, T g , for PEO/PLA multilayer films have been measured by using modulated differential scanning calorimetry and dynamic mechanical analysis, which reveal slight decreases of T g for PLA layer due to the influence of PEO layer. The layer structures of fractured surface of the double-layer films are analyzed on the basis of the observation from scanning electron microscopy, and the existence of interdiffusion areas with irregular boundary between PEO and PLA layers is the key clue to understanding the significant acceleration of G for PLA. The layer-by-layer film method infers promising applications, which might be considered to well replace the blending method.