Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory

Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory

Predicting daily reference evapotranspiration (ETo) plays a significant role in numerous environmental and agricultural applications. It aids in optimizing agricultural practices, enhancing drought resilience, supporting environmental conservation efforts, and providing critical data for research. B...

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Bibliographic Details
Main Authors: Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, Mohammad Ali Ghorbani, Farzan Mohajeri, Aitazaz Ahsan Farooque, Salman Alamery
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Ecological Informatics
Subjects:
Agriculture
Deep learning
ETo
Forecasting
RNN-LSTM
CNN-LSTM
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954125000044
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