Articles | Volume 379
Proc. IAHS, 379, 335–341, 2018
https://doi.org/10.5194/piahs-379-335-2018
Proc. IAHS, 379, 335–341, 2018
https://doi.org/10.5194/piahs-379-335-2018

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.

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Cited articles

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Devineni, N., Sankarasubramanian, A., and Ghosh, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30, 1371–1386, 2007.
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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.