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http://asiair.asia.edu.tw/ir/handle/310904400/115105
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| Title: | Deep learning versus gradient boosting machine for pan evaporation prediction |
| Authors: | Malik, Anurag;Malik, Anurag;Kaur, Mandeep;Saggib, Mandeep Kaur;Rehman, Sufia;Rehman, Sufia;Sajja, Haroon;Sajjad, Haroon;Inyurt, Samed;Inyurt, Samed;Sin, Amandeep;Bhatia, Amandeep Singh;Ahsa, Aitazaz;Farooque, Aitazaz Ahsan;Ou, Atheer Y.;Oudah, Atheer Y.;Mundhe, Zaher;Yaseen, Zaher Mundher |
| Contributors: | 研究發展處學術發展組 |
| Keywords: | Evaporation；deep learning；gradient boosting machine；prediction；Kiashahr；Ranichauri |
| Date: | 2022-01-01 |
| Issue Date: | 2023-03-28 02:24:56 (UTC+0) |
| Publisher: | 亞洲大學 |
| 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. |
| Appears in Collections: | [研究發展處] 期刊論文
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