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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S1574954125000044

Similar Items