Enhanced streamflow prediction using SWAT’s influential parameters: a comparative analysis of PCA-MLR and XGBoost models
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01139-9.pdf
Reference89 articles.
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2. Abbaspour KC (2015) SWAT calibration and uncertainty programs. A user manual 103:17–66. Swiss Federal Institute of Aquatic Science and Technology: Eawag, Duebendorf, Switzerland, pp 1–100
3. Addis HK, Strohmeier S, Ziadat F et al (2016) Modeling streamflow and sediment using SWAT in Ethiopian highlands. Int J Agric Biol Eng 9:51–66. https://doi.org/10.3965/j.ijabe.20160905.2483
4. Akbarian M, Saghafian B, Golian S (2023) Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran. J Hydrol (Amst) 620:129480. https://doi.org/10.1016/j.jhydrol.2023.129480
5. Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA (2023) Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP. Sci Rep 13(1):8984
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