Open Access
Issue |
E3S Web Conf.
Volume 492, 2024
International Conference on Climate Nexus Perspectives: Toward Urgent, Innovative, Sustainable Natural and Technological Solutions for Water, Energy, Food and Environmental Systems (I2CNP 2023)
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Article Number | 01001 | |
Number of page(s) | 10 | |
Section | Artificial Intelligence and Technological Tools Applied to Nexus Water Energy Food Systems | |
DOI | https://doi.org/10.1051/e3sconf/202449201001 | |
Published online | 20 February 2024 |
- Allen, R.G., Pereira, L.S., Raes, D., Smith.: Crop evapotranspiration guidelines for compu-ting crop water requirements. FAO Irrigation and Drainage, Paper No. 56, Food and Agri-culture Organization of the United Nations, Rome. (1998). [Google Scholar]
- Saeid, M., Javad, B., K, K.: Using MARS, SVM, GEP and empirical equations for estima-tion of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture 139, 103–114 (2017). [CrossRef] [Google Scholar]
- Junliang, F., Wenjun, Y., Lifeng, W., Fucang, Z., Huanjie, C., Xiukang, W., Xianghui, L., Youzhen, X.: Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and Forest Meteorology 263, 225–241 (2018). [CrossRef] [Google Scholar]
- Sevim, S.Y., Mladen, T.: Estimation of daily potato crop evapotranspiration using three dif-ferent machine learning algorithms and four scenarios of available meteorological data. Agricultural Water Management 228, 105875 (2020). [CrossRef] [Google Scholar]
- “Grid Search: Selecting Good Support Vector Machine Parameters” par Chris Thornton, et al., publié dans le journal “Proceedings of the Ninth Australian Conference on Neural Networks” (1998). [Google Scholar]
- Vu, M.T., Jardani, A., Massei, N., Fournier, M.: Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network. Journal of Hydrology 597, 125776 (2021). [CrossRef] [Google Scholar]
- Mohamed, A.Y., Alazba, A.A., Mohamed, A.M.: Artificial neural networks versus gene ex-pression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management 163, 110–124 (2016). [CrossRef] [Google Scholar]
- “A Practical Guide to Support Vector Classification” par Chih-Chung Chang et Chih Jen Lin, publié dans le journal “Technical Report, Department of Computer Science and Information Engineering, National Taiwan University” (2001). [Google Scholar]
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