Articles | Volume 379
Proc. IAHS, 379, 335–341, 2018
Proc. IAHS, 379, 335–341, 2018

Pre-conference publication 05 Jun 2018

Pre-conference publication | 05 Jun 2018

Multi-model ensemble hydrological simulation using a BP Neural Network for the upper Yalongjiang River Basin, China

Zhanjie Li et al.

Related authors

Design flood estimation for global river networks based on machine learning models
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999,,, 2021
Short summary
Reservoirs operation and water resources utilization coordination in Hongshuihe basin
Chonghao Li, Kaige Chi, Bo Pang, and Hongbin Tang
Proc. IAHS, 379, 125–129,,, 2018
Short summary
Preface: Innovative Water Resources Management in a Changing Environment – Understanding and Balancing Interactions between Humankind and Nature
Zongxue Xu, Dingzhi Peng, Wenchao Sun, Bo Pang, Depeng Zuo, Andreas Schumann, and Yangbo Chen
Proc. IAHS, 379, 463–464,,, 2018
Physically based distributed hydrological model calibration based on a short period of streamflow data: case studies in four Chinese basins
Wenchao Sun, Yuanyuan Wang, Guoqiang Wang, Xingqi Cui, Jingshan Yu, Depeng Zuo, and Zongxue Xu
Hydrol. Earth Syst. Sci., 21, 251–265,,, 2017
Short summary

Cited articles

Ajami, N., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W014031,, 2007.
Arnold, J., Srinivasan, R., Muttiah, S., and Williams, J.: Large area hydrologic modeling and assessment – Part 1: Model development, J. Am. Water Resour. As., 34, 73–89, 1998.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Devineni, N., Sankarasubramanian, A., and Ghosh, S.: Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations, Water Resour. Res., 44, W094049,, 2008.
Devineni, N., Sankarasubramanian, A., and Ghosh, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30, 1371–1386, 2007.
Short summary
Multi-model ensemble hydrological simulation has been an effective method for improving simulation accuracy. This study explored the feasibility of applying a multi-model ensemble simulation to the upper Yalongjiang River Basin. The results of the BPNN multi-model ensemble simulation are better than that of a single model. Multi-model ensemble simulation should become an important direction in hydrological simulation research.