Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling-Reference-Cited by-同舟云学术

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Physical and artificial intelligence-based hybrid models for rainfall–runoff–sediment process modelling

Published:2023-08-23 Issue:13 Volume:68 Page:1841-1863
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Gelete Gebre¹²^ORCID,Nourani Vahid¹³,Gokcekus Huseyin¹,Gichamo Tagesse²

Affiliation:

1. Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Turkey

2. College of Agriculture and Environmental Science, Arsi University, Asela, Ethiopia

3. Center of Excellence in Hydro informatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2023.2241850

Reference91 articles.

1. Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

2. Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff model: Implications for parameter estimation

3. Daily streamflow prediction using optimally pruned extreme learning machine

4. ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

5. Estimating the Sediment Flux and Budget for a Data Limited Rift Valley Lake in Ethiopia

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment;Water Resources Management;2023-10-17

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