Bias learning improves data driven models for streamflow prediction

Author:

Lin Yongen,Wang Dagang,Meng Yue,Sun Wei,Qiu Jianxiu,Shangguan Wei,Cai Jingheng,Kim Yeonjoo,Dai Yongjiu

Publisher

Elsevier BV

Subject

Earth and Planetary Sciences (miscellaneous),Water Science and Technology

Reference57 articles.

1. A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction;Abbasi;J. Hydrol.,2020

2. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization;Adnan;Knowl. -Based Syst.,2021

3. Comparison of LSSVR, M5RT, NF-GP, and NF-SC models for predictions of hourly wind speed and wind power based on cross-validation;Adnan;Energies,2019

4. Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms;Adnan;Comput. Electron. Agric.,2021

5. Modeling multistep ahead dissolved oxygen concentration using improved support vector machines by a hybrid metaheuristic algorithm;Adnan;Sustainability,2022

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