Arid
DOI10.1007/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
ISSN0920-4741
EISSN1573-1650
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。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Amani, Shima]的文章
[Shafizadeh-Moghadam, Hossein]的文章
[Morid, Saeid]的文章
百度学术
百度学术中相似的文章
[Amani, Shima]的文章
[Shafizadeh-Moghadam, Hossein]的文章
[Morid, Saeid]的文章
必应学术
必应学术中相似的文章
[Amani, Shima]的文章
[Shafizadeh-Moghadam, Hossein]的文章
[Morid, Saeid]的文章
相关权益政策
暂无数据
收藏/分享

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。