Next Article in Journal
Multiple Changes in the Hydrologic Regime of the Yangtze River and the Possible Impact of Reservoirs
Previous Article in Journal
Strategic Framework for Sustainable Management of Drainage Systems in Semi-Arid Cities: An Iraqi Case Study
Article Menu

Export Article

Open AccessArticle
Water 2016, 8(9), 407; doi:10.3390/w8090407

Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling

1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editor: Y. Jun Xu
Received: 14 July 2016 / Revised: 12 September 2016 / Accepted: 14 September 2016 / Published: 19 September 2016
View Full-Text   |   Download PDF [7245 KB, uploaded 19 September 2016]   |  

Abstract

A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC) model with artificial neural networks (ANNs). In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation). The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River. View Full-Text
Keywords: variable infiltration capacity model; artificial neural networks; ensemble predictions; bias-correction; routing model variable infiltration capacity model; artificial neural networks; ensemble predictions; bias-correction; routing model
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Meng, C.; Zhou, J.; Tayyab, M.; Zhu, S.; Zhang, H. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling. Water 2016, 8, 407.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top