Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

作者：

Highlights：

• ELM-PSOGWO is compared with the ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA methods.

• ELM-PSOGWO method found more successful than the other benchmarked models.

• ELM-PSOGWO method also provided the lowest AMRE in peak streamflow estimation.

• ELM-PSOGWO significantly improved the RMSE of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA.

摘要

•ELM-PSOGWO is compared with the ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA methods.•ELM-PSOGWO method found more successful than the other benchmarked models.•ELM-PSOGWO method also provided the lowest AMRE in peak streamflow estimation.•ELM-PSOGWO significantly improved the RMSE of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA.

论文关键词：Streamflow prediction,Extreme learning machine,Particle swarm optimization,Grey wolf optimization,Hybrid metaheuristic approach

论文评审过程：Received 5 April 2021, Revised 27 July 2021, Accepted 9 August 2021, Available online 12 August 2021, Version of Record 1 September 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107379