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: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2020
• Subjects: recurrent neural networks; deep learning; irrigation scheduling; Penman–Monteith; physical hydrology components; water cycle budgeting; Canada; North Cape; Prince Edward Island
• Online Access: https://doi.org/10.3390/app10051621

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...