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Artificial neural network (ANN)-based data-driven model is an effective and robust tool for multi-input single-output (MISO) system simulation task. However, there are several conundrums which deteriorate the performance of the ANN model. These problems include the hard task of topology design, parameter training, and the balance between simulation accuracy and generalization capability. In order to overcome conundrums mentioned above, a novel hybrid data-driven model named KEK was proposed in this paper. The KEK model was developed by coupling the K-means method for input clustering, ensemble back-propagation (BP) ANN for output estimation, and K-nearest neighbor (KNN) method for output error estimation. A novel calibration method was also proposed for the automatic and global calibration of the KEK model. For the purpose of intercomparison of model performance, the ANN model, KNN model, and proposed KEK model were applied for two applications including the Peak benchmark function simulation and the real-world electricity system daily total load forecasting. The testing results indicated that the KEK model outperformed other two models and showed very good simulation accuracy and generalization capability in the MISO system simulation tasks. [ABSTRACT FROM AUTHOR]

A new hybrid data-driven model named PBK has been proposed to improve the event-based rainfall-runoff simulation. The PBK is developed by coupling partial mutual information-based input variable selection (IVS), ensemble back-propagation neural network (EBPNN)-based discharge forecasting and K-nearest neighbor algorithm-based discharge error forecasting. This model is proposed for solving the hard problem of how to implement non-updating rainfall-runoff simulation by data-driven models. For the purpose of solving the hard problems, the PBK model has the following innovations and improvements: (1) a newly proposed non-updating modeling approach without the using of the real-time information and can obtain higher simulation accuracy; (2) a newly proposed IVS scheme and a newly proposed candidate rainfall input set to ensure the adequacy and parsimony of the rainfall and antecedent discharge input variables; and (3) a newly proposed calibration method for the EBPNN to ensure higher simulation accuracy and better generalization property. This method is a combination of the NGSA-II, Levenberg-Marquardt algorithm, and the AIC-based combination weights generating method. For the purpose of comparing simulation accuracy with traditional non-updating data-driven models, a back-propagation neural network model (PB_R) and a linear model (CLS) were also studied. This study utilized event flood data of Dongwan catchment for intercomparisons between different models. The simulation results indicated that the PBK model outperforms other data-driven models and has higher accuracy and better forecasting capability. [ABSTRACT FROM AUTHOR]