Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

• Published in: Applied Sciences
• Main Authors: Hassan Afzaal, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, Travis Esau
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: recurrent neural networks; deep learning; irrigation scheduling; penman–monteith; physical hydrology components; water cycle budgeting; Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Biology (General); QH301-705.5; Physics; QC1-999; Chemistry; QD1-999; Canada; North Cape; Prince Edward Island
• Online Access: https://doi.org/10.3390/app10051621; https://doaj.org/article/417406d8a7c34a549a923b5d5d6e2e3c

Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorolog...