Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1007/s11269-023-03670-2 |
Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration | |
Amani, Shima; Shafizadeh-Moghadam, Hossein; Morid, Saeid | |
通讯作者 | Shafizadeh-Moghadam, H |
来源期刊 | WATER RESOURCES MANAGEMENT
![]() |
ISSN | 0920-4741 |
EISSN | 1573-1650 |
出版年 | 2024 |
卷号 | 38期号:6页码:1921-1942 |
英文摘要 | The current study evaluated the accuracy of four machine learning (ML) models and thirteen experimental methods calibrated to estimate reference evapotranspiration (ET0) in arid and semi-arid regions. Various scenarios were examined utilizing meteorological data and FAO56-PM as a benchmark. According to the results, the ML models outperformed the experimental methods on both daily and monthly scales. Among the ML models, artificial neural networks (ANNs), generalized additive model (GAM), random forest (RF), and support vector machine (SVM), respectively, demonstrated higher accuracy on a monthly scale, while ANNs, SVM, RF, and GAM exhibited greater accuracy on a daily scale. Notably, ANNs and SVM achieved high accuracy even with a limited number of variables. Conversely, RF showed improved accuracy with an increased number of variables. Comparing the ML and experimental models with equivalent inputs revealed that ANN with inputs similar to Valiantzas-1 performed better on a monthly scale, while SVM with inputs akin to Valiantzas-3 showed superior performance on a daily scale. Our findings suggest that average temperature, wind speed, and sunshine hours contribute significantly to the accuracy of ML models. Consequently, these ML models can serve as an alternative to the FAO56-PM method for estimating ET0. |
英文关键词 | Reference evapotranspiration FAO-56PM Machine learning Experimental models |
类型 | Article |
语种 | 英语 |
开放获取类型 | Green Submitted |
收录类别 | SCI-E |
WOS记录号 | WOS:001157144700005 |
WOS关键词 | CLIMATIC DATA ; NEURAL-NETWORK ; TEMPERATURE ; EVAPORATION ; SVM ; REGRESSION ; EQUATIONS ; RADIATION ; HUMIDITY ; SOLAR |
WOS类目 | Engineering, Civil ; Water Resources |
WOS研究方向 | Engineering ; Water Resources |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/405920 |
推荐引用方式 GB/T 7714 | Amani, Shima,Shafizadeh-Moghadam, Hossein,Morid, Saeid. Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration[J],2024,38(6):1921-1942. |
APA | Amani, Shima,Shafizadeh-Moghadam, Hossein,&Morid, Saeid.(2024).Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration.WATER RESOURCES MANAGEMENT,38(6),1921-1942. |
MLA | Amani, Shima,et al."Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration".WATER RESOURCES MANAGEMENT 38.6(2024):1921-1942. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。