干旱区生态环境知识资源中心(Arid): Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment

Arid

DOI	10.1061/(ASCE)IR.1943-4774.0001714
	Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment
	Beiranvand, Javad Pirvali; Ghamghami, Mahdi
通讯作者	Beiranvand, JP
来源期刊	JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
ISSN	0733-9437
EISSN	1943-4774
出版年	2022
卷号	148 期号:11
英文摘要	Studies of the estimation of reference evapotranspiration (ET0) in Iran are mostly related to areas with humid and semiarid climates and less related to arid areas. On the other hand, few studies in arid regions have reported high root-mean square error (RMSE) values. However, these regions make an important contribution to agricultural production, and thus, water management of these regions is crucial. It motivated the implementation of such a study in an arid environment of Karaj, Iran, as a case study, in order to estimate daily ET0 with as much accuracy as possible. To achieve this purpose, the performance of 21 known models estimating ET0, including 9 empirical models and 12 machine learning (ML) models, were evaluated. The method provided by the food and agriculture organization (FAO) known as FAO-56 Penman-Monteith (FPM) was regarded as the main reference method for measuring ET0. A new climate data set related to the period of 2005-2020 (April-September) was used to calibrate and cross-validate models. In fact, this study intended to develop an approach for simulating day-to-day variations in ET0 in arid environments by benefitting minimal weather data (i.e., temperature, humidity, and wind speed) for practical purposes because most regions suffer from a lack of weather data, especially radiation data. Therefore, the performance of the best models calibrated in Karaj station was also validated based on data recorded in a research field with a similar climate during 2020-2021. The cross-validated results showed that the deep-learning (DL) model had the lowest RMSE as well as the highest R-2 compared with other models. As averaged over all months in Karaj, the DL model exhibited a RMSE 64% less than the best-calibrated empirical model (i.e., Valiantzas-VTS), which is a solar radiation-based model. Furthermore, the improvements arising from using the DL model were more considerable in the extremes than in the middle values. The findings of the field research also demonstrated that the approach developed in the current study would be beneficial at locations other than the calibration station. This approach requires few inputs, is independent of solar radiation data, and also has the lowest RMSE compared with that of other studies. For future studies, the combination of the daily ET0 estimator developed in this study with a recently developed empirical approach to estimate crop coefficients is suggested to manage irrigation on croplands if arid regions. (C) 2022 American Society of Civil Engineers.
英文关键词	Day-to-day variation Deep learning (DL) Extremes Irrigation scheduling Minimal weather data
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000853929600006
WOS关键词	LIMITED METEOROLOGICAL DATA ; ARTIFICIAL NEURAL-NETWORK ; CLIMATIC DATA ; REGRESSION ; SVM ; PRECIPITATION ; TEMPERATURE ; PREDICTION ; REGIONS ; ELM
WOS类目	Agricultural Engineering ; Engineering, Civil ; Water Resources
WOS研究方向	Agriculture ; Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393541
推荐引用方式 GB/T 7714	Beiranvand, Javad Pirvali,Ghamghami, Mahdi. Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment[J],2022,148(11).
APA	Beiranvand, Javad Pirvali,&Ghamghami, Mahdi.(2022).Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment.JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING,148(11).
MLA	Beiranvand, Javad Pirvali,et al."Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment".JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 148.11(2022).