A new hybrid data-driven model for event-based rainfall–runoff simulation

A new hybrid data-driven model for event-based rainfall–runoff simulation 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

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Springer London
Copyright © 2016 by The Natural Computing Applications Forum
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
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