干旱区生态环境知识资源中心(Arid): Comparison of machine learning and dynamic models for predicting actual vapour pressure when psychrometric data are unavailable

Arid

DOI	10.1016/j.jhydrol.2022.127989
	Comparison of machine learning and dynamic models for predicting actual vapour pressure when psychrometric data are unavailable
	Qiu, Rangjian; Li, Longan; Wu, Lifeng; Agathokleous, Evgenios; Liu, Chunwei; Zhang, Baozhong
通讯作者	Zhang, BZ
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2022
卷号	610
英文摘要	Information of actual vapour pressure (e(a)) is frequently required in many disciplines. However, psychrometric data required to calculate e(a) are often not readily available. Hence, it is of great importance to develop models to estimate e(a) when psychrometric data are unavailable. Here, five machine learning models were developed for estimating e(a), viz. extreme gradient boosting (XGBoost), extreme learning machine (ELM), kernel-based nonlinear extension of Arps decline (KNEA), multiple adaptive regression splines (MARS), and support vector machine (SVM) models. Their performance was also compared to a dynamic model proposed recently, which estimates e(a) by adjusting dew point temperature from minimum temperature (T-min) with dynamic correction factor. Three input combinations using only temperature data (i.e. T-min and mean temperature (T-mean)) were considered in the machine learning models. The meteorological data collected from 1,188 stations across six climate zones were used to develop and assess the models. The overall results revealed that the dynamic and machine learning models offered satisfactory e(a) estimates spanning from hyper arid to humid climates. However, the accuracy of the dynamic model was lower than all machine learning algorithms using either only T-min or combinations of T-mean and T-min in all climate zones. The machine learning models using T-mean and T-min were superior to those using only T-mean or T-min . There were comparable performances among the ELM, KNEA, MARS, and SVM models with various input variables; however, the XGBoost model incorporating T-mean and T-min produced the best accuracy. The computational demand was least for the ELM model, followed by the XGBoost model. Considering the accuracy and computational demand, the XGBoost model is recommended for predicting daily and monthly e(a) from hyper arid to humid climates when historical data are prior known. When there are no historical data, we recommend using the global XGBoost model incorporating T-mean , T-min , and aridity index for estimating daily and monthly e(a) from arid to humid regions, and using the dynamic model in hyper-arid regions.
英文关键词	Aridity index Extreme gradient boosting Extreme learning machine Global extreme gradient boosting Multiple adaptive regression splines Support vector machine
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000813500800001
WOS关键词	DAILY REFERENCE EVAPOTRANSPIRATION ; SUPPORT VECTOR MACHINE ; GLOBAL SOLAR-RADIATION ; ARTIFICIAL-INTELLIGENCE MODELS ; PENMAN-MONTEITH MODEL ; ARPS DECLINE MODEL ; EMPIRICAL EQUATIONS ; TEMPERATURE ; CLIMATES ; METHODOLOGY
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393492
推荐引用方式 GB/T 7714	Qiu, Rangjian,Li, Longan,Wu, Lifeng,et al. Comparison of machine learning and dynamic models for predicting actual vapour pressure when psychrometric data are unavailable[J],2022,610.
APA	Qiu, Rangjian,Li, Longan,Wu, Lifeng,Agathokleous, Evgenios,Liu, Chunwei,&Zhang, Baozhong.(2022).Comparison of machine learning and dynamic models for predicting actual vapour pressure when psychrometric data are unavailable.JOURNAL OF HYDROLOGY,610.
MLA	Qiu, Rangjian,et al."Comparison of machine learning and dynamic models for predicting actual vapour pressure when psychrometric data are unavailable".JOURNAL OF HYDROLOGY 610(2022).