Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-021-15563-1.pdf
Reference42 articles.
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2. Adnan RM, Liang Z, Heddam S, Zounemat-Kermani M, Kisi O, Li B (2020) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydrometeorological data as inputs. J Hydrol 586:124371. https://doi.org/10.1016/j.jhydrol.2019.124371
3. Araghinejad S, Fayaz N, Hosseini-Moghari SM (2018) Development of a hybrid data driven model for hydrological estimation. Water Resour Manage 32:3737–3750. https://doi.org/10.1007/s11269-018-2016-3
4. Awchi TA (2014) River discharges forecasting in Northern Iraq using different ANN techniques. Water Resour Manag 28(3):801–814. https://doi.org/10.1007/s11269-014-0516-3
5. Bergström S (1976) Development and application of a conceptual model for Scandinavian catchments. Swedish Meteorological and Hydrological Institute, Report RHO No. 7, Norrköping, Sweden
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