NDLI: Mid-short-term daily runoff forecasting by ANNs and multiple process-based hydrological models

Content Provider	IEEE Xplore Digital Library
Author	Jingwen Xu Junfang Zhao Wanchang Zhang Zhongda Hu Ziyan Zheng
Copyright Year	2009
Description	Author affiliation: Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China (Junfang Zhao) \|\| Key Laboratory of Regional Climate-Environment Research for Temperature East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China (Wanchang Zhang; Ziyan Zheng) \|\| College of Resources and Environment, Sichuan Agricultural University, Yaan 625014, China (Jingwen Xu; Zhongda Hu)
Abstract	In recent decades, the daily runoff forecasting based on artificial Neural Network (ANN) models has become quite important to deliver sustainable use and effective planning and management of water resources. The performance of the existent ANN models for 1 day in advance forecasting are frequently reported. However, the mid-term forecasting by ANN is scarce in the literature. In this study, a feed forward network trained with a back-propagation learning algorithm (BP-ANN) is used to construct a mid-short-term daily runoff forecasting system. ANN models having various input variables were constructed and the best structure was investigated. Moreover, the performance of ANN models and multiple process-based rainfall-runoff models, including Xinanjiang, ESSI, SWAT and XXT, is compared. Baohe River basin, located in central China, is chosen as a case study area. The results show that in general the performance of ANN models decrease as the lead time increase when the lead time is less than 11 days; while it varies slightly with lead time when the lead time is larger than 11 days. The ANN model with an appropriate combination of stream flow, precipitation as input variables performs much better than all the process-based rainfall-runoff models in terms of Nash-Sutcliffe efficiency for mid-short-term daily runoff forecasting.
Starting Page	526
Ending Page	529
File Size	1369197
Page Count	4
File Format	PDF
ISBN	9781424450749
DOI	10.1109/YCICT.2009.5382440
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	ZigBee Irrigation ANN Wireless application protocol Computerized monitoring Predictive models Control systems Application software Process-based models Wireless sensor networks Network topology XXT Large-scale systems Xinanjiang Daily runoff forecast
Content Type	Text
Resource Type	Article

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