Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models-Reference-Cited by-同舟云学术

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Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models

Published:2023-05-19 Issue:7 Volume:68 Page:1050-1063
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Sabanci Dilek¹^ORCID,Yurekli Kadri²^ORCID,Comert Mehmet Murat²^ORCID,Kilicarslan Serhat³^ORCID,Erdogan Muberra²^ORCID

Affiliation:

1. Department of Mathematics, Science and Letters, University of Tokat Gaziosmanpasa, Tokat, Türkiye

2. Department of Biosystem Engineering, Agriculture, University of Tokat Gaziosmanpasa, Tokat, Türkiye

3. Department of Software Engineering, Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Balikesir, Türkiye

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2023.2203824

Reference74 articles.

1. Extreme Learning Machines: A new approach for prediction of reference evapotranspiration

2. Evaporation and Evapotranspiration

3. Neural computing modeling of the reference crop evapotranspiration

4. Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

5. RCP4.5 ve RCP8.5 iklim senaryolarına göre konutlarda iklim değişikliği etki değerlendirmesi

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1. GEE RET: Cloud-based reference evapotranspiration calculation with google earth engine;Theoretical and Applied Climatology;2024-09-02

2. Data Mining Technology of Country’s Digital Development Level Assessing for Economic Development and Sustainable Growth: Multivariate Adaptive Regression Spline;Journal of the Knowledge Economy;2024-05-27

3. Integrated approach to assessing strength in slag-based geopolymer mortars: experimental study and modeling with advanced techniques;Journal of Materials Science;2024-04-21

4. A new strategy for prediction of water qualitative and quantitative parameters by deep learning-based models with determination of modelling uncertainties;Hydrological Sciences Journal;2023-12-19

5. A novel method for estimating daily evapotranspiration based on one, two, and three-day meteorological records using the long short-term memory model combined with feedforward neural networks;Earth Science Informatics;2023-11-14

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