A combined rotated general regression neural network method for river flow forecasting. Issue 4 (11th March 2016)

• Record Type: Journal Article
• Authors: Yin, Sun; Tang, Deshan; Jin, Xin; Chen, Weiwei; Pu, Nannan
• Is Part Of: Hydrological sciences journal. Volume 61:Issue 4(2016)
• Page Start: 669
• Page End: 682
• Publication Date: 2016-03-11
• Subjects: rotated general regression neural network -- monthly river flow forecasting -- combination methodology -- arid and semi-arid region; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2014.944525
• Languages: English
• ISSNs: 0262-6667

Abstract

ABSTRACT: This study focused on the performance of the rotated general regression neural network (RGRNN), as an enhancement of the general regression neural network (GRNN), in monthly-mean river flow forecasting. The study of forecasting of monthly mean river flows in Heihe River, China, was divided into two steps: first, the performance of the RGRNN model was compared with the GRNN model, the feed-forward error back-propagation (FFBP) model and the soil moisture accounting and routing (SMAR) model in their initial model forms; then, by incorporating the corresponding outputs of the SMAR model as an extra input, the combined RGRNN model was compared with the combined FFBP and combined GRNN models. In terms of model efficiency index, R 2, and normalized root mean squared error, NRMSE, the performances of all three combined models were generally better than those of the four initial models, and the RGRNN model performed better than the GRNN model in both steps, while the FFBP and the SMAR were consistently the worst two models. The results indicate that the combined RGRNN model could be a useful river flow forecasting tool for the chosen arid and semi-arid region in China.Editor D. Koutsoyiannis;Associate editor not assigned