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 | 01002 | |
Number of page(s) | 7 | |
Section | Artificial Intelligence and Technological Tools Applied to Nexus Water Energy Food Systems | |
DOI | https://doi.org/10.1051/e3sconf/202449201002 | |
Published online | 20 February 2024 |
- Allen, R.G., Pereira, L.S., Howell, T.A., Jensen, M.E., 2011. Evapotranspiration informationreporting: I. Factors governing measurement accuracy. Agric. Water Manage. 98. [Google Scholar]
- Allen, R.G., Pereira, L.S., Raes, D., Smith.: Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage, Paper No. 56, Food and Agriculture Organization of the United Nations, Rome. (1998). [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]
- 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]
- Haykin, S. Neural Networks: A Comprehensive Foundation. Prentice Hall (1994). [Google Scholar]
- Karim, F., & Majumdar, S. (2018). A review on the application of deep learning in system health management. IEEE Access, 6, 69000-69019. [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]
- Mandeep, K.S., Sushma, J.: Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning. Computers and Electronics in Agriculture 156, 387–398 (2018). [Google Scholar]
- Patrícia, D.O.L., Marcos, A.A, Petrônio, C.L.S, Frederico, G.G, Reference evapotranspira-tion time series forecasting with ensemble of convolutional neural networks, Computers and Electronics in Agriculture 177, 105700 (2020). [CrossRef] [Google Scholar]
- Lucas, B.F., Fernando, F.D.C.: New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning, Agricultural Water Management 234, 106113 (2020). [CrossRef] [Google Scholar]
- Lipton, Z.C., Berkowitz, J., Elkan, C.: A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv, 1506.00019 (2015). [Google Scholar]
- Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Comput, 1735–1780 (1997). [CrossRef] [PubMed] [Google Scholar]
- Hu, C., Wu, Q., Li, H., Jian, S., Li, N., Lou, Z. Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10, 1543 (2018). [CrossRef] [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]
- http://colah.github.io/posts/2015-08-Understanding-LSTMs/. (2015). [Google Scholar]
- Kingma, D.P., Ba, J.: Adam: [Google Scholar]
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