Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms-Reference-Cited by-同舟云学术

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms

Published:2022-01 Issue:2 Volume:36 Page:699-716
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Khosravi Khabat^ORCID,Golkarian Ali,Tiefenbacher John P.

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/s11269-021-03051-7.pdf

Reference61 articles.

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3. Adnan R, Liang Z, Heddam S, Zounemat-Kermani M, Kisi O, Li B (2019) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J of Hyd. https://doi.org/10.1016/j.jhydrol.2019.124371

4. Adnan R, Yuan X, Kisi O, Adnan M, Mehmood A (2018) Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station. Water Resour Manag 32:4469–4486. https://doi.org/10.1007/s11269-018-2033-2

5. Ayele G, Teshale E, Yu B, Rutherfurd I, Jeong J (2017) Streamflow and sediment yield prediction for watershed prioritization in the upper blue nile river basin, ethiopia. Water. https://doi.org/10.3390/w9100782

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