Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks
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DOI: 10.1016/j.agwat.2021.107040
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- Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
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- Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.
- Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
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- Fuentes, Sigfredo & Ortega-Farías, Samuel & Carrasco-Benavides, Marcos & Tongson, Eden & Gonzalez Viejo, Claudia, 2024. "Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling," Agricultural Water Management, Elsevier, vol. 297(C).
- Zhang, Yixiao & He, Tao & Liang, Shunlin & Zhao, Zhongguo, 2023. "A framework for estimating actual evapotranspiration through spatial heterogeneity-based machine learning approaches," Agricultural Water Management, Elsevier, vol. 289(C).
- Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
- Valipour, Mohammad & Khoshkam, Helaleh & Bateni, Sayed M. & Jun, Changhyun & Band, Shahab S., 2023. "Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States," Agricultural Water Management, Elsevier, vol. 283(C).
- Xing, Liwen & Cui, Ningbo & Liu, Chunwei & Zhao, Lu & Guo, Li & Du, Taisheng & Zhan, Cun & Wu, Zongjun & Wen, Shenglin & Jiang, Shouzheng, 2022. "Estimation of daily apple tree transpiration in the Loess Plateau region of China using deep learning models," Agricultural Water Management, Elsevier, vol. 273(C).
- Feng, Jiaojiao & Wang, Weizhen & Xu, Feinan & Wang, Shengtang, 2024. "Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces," Agricultural Water Management, Elsevier, vol. 291(C).
- Yan Guo & Wei Tang & Guanghua Hou & Fei Pan & Yubo Wang & Wei Wang, 2021. "Research on Precipitation Forecast Based on LSTM–CP Combined Model," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
- Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
- Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).
- Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2022. "Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes," Agricultural Water Management, Elsevier, vol. 261(C).
- Karbasi, Masoud & Jamei, Mehdi & Ali, Mumtaz & Malik, Anurag & Chu, Xuefeng & Farooque, Aitazaz Ahsan & Yaseen, Zaher Mundher, 2023. "Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 290(C).
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Keywords
Evapotranspiration; Ensemble models; Deep learning; LSTM; NARX; Forecasting;All these keywords.
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