Integrating Support Vector Machines with Different Ensemble Learners for Improving Streamflow Simulation in an Ungauged Watershed-Reference-Cited by-同舟云学术

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Integrating Support Vector Machines with Different Ensemble Learners for Improving Streamflow Simulation in an Ungauged Watershed

Published:2023-12-05 Issue:2 Volume:38 Page:553-567
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Takai Eddine Yahi,Nadir Marouf,Sabah Sehtal,Jaafari Abolfazl

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11269-023-03684-w.pdf

Reference52 articles.

1. Adnan RM, Jaafari A, Mohanavelu A, Kisi O, Elbeltagi A (2021) Novel ensemble forecasting of streamflow using locally weighted learning algorithm. Sustainability 13(11):5877

2. Adnan Ikram R, Khan I, Moayedi H, Dehrashid AA, Elkhrachy I, Le BN (2023) Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environ Dev Sustain 1–33

3. Atashi V, Barati R, Lim YH (2023) Improved river flood routing with spatially variable exponent muskingum model and sine cosine optimization algorithm. Environ Process 10(42):1–20

4. Blöschl G, Hall J, Viglione A, Perdigão RA, Parajka J, Merz B et al (2019) Changing climate both increases and decreases European river floods. Nature 573(7772):108–111

5. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

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