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Deep learning versus gradient boosting machine for pan evaporation prediction.

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Bibliographic Details
Title:	Deep learning versus gradient boosting machine for pan evaporation prediction.
Authors:	Malik, Anurag, Saggi, Mandeep Kaur, Rehman, Sufia, Sajjad, Haroon, Inyurt, Samed, Bhatia, Amandeep Singh, Farooque, Aitazaz Ahsan, Oudah, Atheer Y., Yaseen, Zaher Mundher
Source:	Engineering Applications of Computational Fluid Mechanics; Dec2022, Vol. 16 Issue 1, p570-587, 18p
Subject Terms:	DEEP learning, METEOROLOGICAL stations, ATMOSPHERIC temperature, PREDICTION models, FORECASTING, MACHINERY
Geographic Terms:	IRAN, UTTARAKHAND (India)
Abstract:	In the present study, two innovative techniques namely, Deep Learning (DL) and Gradient boosting Machine (GBM) models are developed based on a maximum air temperature 'univariate modeling scheme' for modeling the monthly pan evaporation (Epan) process. Monthly air temperature and pan evaporation are used to build the predictive models. These models are used for evaluating the evaporation prediction for the Kiashahr meteorological station located in the north of Iran and Ranichauri station positioned in Uttarakhand State of India. Findings indicated that the deep learning model was found best at Kiashahr station for testing datasets MAE (0.5691, mm/month), RMSE (0.7111, mm/month), NSE (0.7496), and IOA (0.9413). It can be concluded that in the semi-arid climate of Iran both of the used methods had the good capability in modeling of monthly Epan. However, DL predicted monthly Epan better than GBM. Moreover, the highest accuracy of the deep learning model was also observed for the Ranichauri station in terms of MAE = 0.3693 mm/month, RMSE = 0.4357 mm/month, NSE = 0.8344, & IOA = 0.9507 in testing stage. Overall, results expose the superior performance of DL-based models for both study stations and can also be utilized for various other environmental modeling. [ABSTRACT FROM AUTHOR]
	Copyright of Engineering Applications of Computational Fluid Mechanics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Complementary Index

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