On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s12517-022-11045-x.pdf
Reference89 articles.
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2. Adnan RM, Petroselli A, Heddam S, Santos CAG, Kisi O (2021) Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stoch Environ Res Risk Assess 35(3):597–616. https://doi.org/10.1007/s00477-020-01910-0
3. Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Wan Mohtar WHM, Hussain A (2015) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29(4):1231–1245. https://doi.org/10.1007/s11269-014-0870-1
4. Alizadeh A, Rajabi A, Shabanlou S, Yaghoubi B, Yosefvand F (2021) Modeling long-term rainfall-runoff time series through wavelet-weighted regularization extreme learning machine. Earth Sci Inform 14(2):1047–1063. https://doi.org/10.1007/s12145-021-00603-8
5. Angarita-Zapata JS, Masegosa AD, Triguero I (2020) Evaluating automated machine learning on supervised regression traffic forecasting problems. In: Llanes Santiago O, Cruz Corona C, Silva Neto AJ, Verdegay JL (eds) Computational intelligence in emerging technologies for engineering applications. Springer, Cham, pp 187–204. https://doi.org/10.1007/978-3-030-34409-2_11
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