Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition

Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd...

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Bibliographic Details
Main Authors: Masoud Karbasi, Mumtaz Ali, Sayed M. Bateni, Changhyun Jun, Mehdi Jamei, Zaher Mundher Yaseen
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Alexandria Engineering Journal
Subjects:
Pan Evaporation
Forecasting
Machine Learning
Deep Learning
Boruta Feature Selection
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016823010499

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http://www.sciencedirect.com/science/article/pii/S1110016823010499

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