Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China

  • Muhammad Tayyab School of Hydropower & Information Engineering, Huazhong University of Science & Technology, Wuhan, China
  • Jianzhong Zhou School of Hydropower & Information Engineering, Huazhong University of Science & Technology, Wuhan, China
  • Xiaofan Zeng School of Hydropower & Information Engineering, Huazhong University of Science & Technology, Wuhan, China
  • Rana Adnan School of Hydropower & Information Engineering, Huazhong University of Science & Technology, Wuhan, China

Abstract

Flood prediction methods play an important role in providing early warnings to government offices. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied three different types of Artificial Neural Networks (ANN) and an autoregressive model to study the Jinsha river basin (JRB), in the upper part of the Yangtze River in China. The three ANN techniques include feedforward back propagation neural networks (FFBPNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBFNN). Artificial Neural Networks (ANN) has shown Great deal of accuracy as compared to statistical autoregressive (AR) model because statistical model cannot able to simulate the non-linear pattern. The results varied across the cases used in the study; based on available data and the study area, FFBPNN showed the best applicability, compared to other techniques.

Downloads

Download data is not yet available.
Published
2016-03-30
How to Cite
Tayyab, M., Zhou, J., Zeng, X., & Adnan, R. (2016). Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China. European Scientific Journal, ESJ, 12(9), 108. https://doi.org/10.19044/esj.2016.v12n9p108