Abstract. Inflow forecasting plays an essential role in reservoir management and operation. The impacts of climate change and human activities have made accurate inflow prediction increasingly difficult, especially for longer lead times. In this study, a new hybrid inflow forecast framework – using the ERA-Interim reanalysis data set as input and adopting gradient-boosting regression trees (GBRT) and the maximal information coefficient (MIC) – is developed for multistep-ahead daily inflow forecasting. Firstly, the ERA-Interim reanalysis data set provides more information for the framework, allowing it to discover inflow for longer lead times. Secondly, MIC can identify an effective feature subset from massive features that significantly affects inflow; therefore, the framework can reduce computational burden, distinguish key attributes from unimportant ones and provide a concise understanding of inflow. Lastly, GBRT is a prediction model in the form of an ensemble of decision trees, and it has a strong ability to more fully capture nonlinear relationships between input and output at longer lead times. The Xiaowan hydropower station, located in Yunnan Province, China, was selected as the study area. Six evaluation criteria, namely the mean absolute error (MAE), the root-mean-squared error (RMSE), the Pearson correlation coefficient (CORR), Kling–Gupta efficiency (KGE) scores, the percent bias in the flow duration curve high-segment volume (BHV) and the index of agreement (IA) are used to evaluate the established models utilizing historical daily inflow data (1 January 2017–31 December 2018). The performance of the presented framework is compared to that of artificial neural network (ANN), support vector regression (SVR) and multiple linear regression (MLR) models. The results indicate that reanalysis data enhance the accuracy of inflow forecasting for all of the lead times studied (1–10 d), and the method developed generally performs better than other models, especially for extreme values and longer lead times (4–10 d).

The paper was intended to address the deficiencies of quality and safety appraisement methods for mobile power pack (MPP) sold on e-commerce platforms. Based on the comprehensive index method, the quality index evaluation model of MPP under e-commerce platform was constructed by combining principal component analysis (PCA), cluster analysis, and analytic hierarchy process (AHP). The index system firstly analyzed the factors related to the quality and safety of MPP and determined the original index. Then, the original index was optimized by combining PCA and clustering analysis, and the index system of the index evaluation model was determined. Finally, the weights of various indexes were determined by AHP, to complete the quality index evaluation model for MPP sold under the e-commerce platform.

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Abstract. Inflow forecasting plays an essential role in reservoir management and operation. The impacts of climate change and human activities make accurate inflow prediction increasingly difficult, especially for longer lead times. In this study, a new hybrid inflow forecast framework with ERA-Interim reanalysis data as input, adopting gradient boosting regression trees (GBRT) and the maximum information coefficient (MIC) was developed for multi-step ahead daily inflow forecasting. Firstly, the ERA-Interim reanalysis dataset provides enough information for the framework to discover inflow for longer lead times. Secondly, MIC can identify effective feature subset from massive features that significantly affects inflow so that the framework can avoid over-fitting, distinguish key attributes with unimportant ones and provide a concise understanding of inflow. Lastly, the GBRT is a prediction model in the form of an ensemble of decision trees and has a strong ability to capture nonlinear relationships between input and output in long lead times more fully. The Xiaowan hydropower station located in Yunnan Province, China is selected as the study area. Four evaluation criteria, the mean absolute error (MAE), the root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NSE) and the Pearson correlation coefficient (CORR), were used to evaluate the established models using historical daily inflow data (1/1/2017–31/12/2018). Performance of the presented framework was compared to that of artificial neural networks (ANN), support vector regression (SVR) and multiple linear regression (MLR) models. The experimental results indicate that the developed method generally performs better than other models and significantly improves the accuracy of inflow forecasting at lead times of 5–10 days. The reanalysis data also enhances the accuracy of inflow forecasting except for forecasts that are one-day ahead.