Deep learning versus gradient boosting machine for pan evaporation prediction
Yazarlar (9)
Punjab Agricultural University, Hindistan
Thapar Institute Of Engineering & Technology, Hindistan
Jamia Millia Islamia, Hindistan
Jamia Millia Islamia, Hindistan
Prof. Dr. Samed İNYURT
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Chitkara University, Punjab, Hindistan
University Of Prince Edward Island, Kanada
University Of Thi-Qar, Irak
South Ural State University, Rusya Federasyonu
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Engineering Applications of Computational Fluid Mechanics |
| Dergi ISSN | 1994-2060 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2022 |
| Cilt No | 16 |
| Sayı | 1 |
| Sayfalar | 570 / 587 |
| DOI Numarası | 10.1080/19942060.2022.2027273 |
| Makale Linki | http://dx.doi.org/10.1080/19942060.2022.2027273 |
| Özet |
| 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 (E pan) 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 E pan. However, DL predicted monthly E pan 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. |
| Anahtar Kelimeler |
| deep learning | Evaporation | gradient boosting machine | Kiashahr | prediction | Ranichauri |
| Atıf Sayıları | |
| WoS | 50 |
| SCOPUS | 84 |
| Google Scholar | 104 |