干旱区生态环境知识资源中心(Arid): Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models

Arid

DOI	10.1061/JHYEFF.HEENG-6232
	Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models
	Alsumaiei, Abdullah A.
通讯作者	Alsumaiei, AA
来源期刊	JOURNAL OF HYDROLOGIC ENGINEERING
ISSN	1084-0699
EISSN	1943-5584
出版年	2024
卷号	29 期号:5
英文摘要	Evaporation is considered to be one of the most influential hydrological processes, contributing significantly to water loss within the hydrological cycle. This study aimed to address the challenge of modeling daily pan evaporation in arid climates, where harsh hydroclimatic conditions hinder modeling efficacy. In such climates, annual pan evaporation rates exceed 3,500 mm, exacerbating water scarcity in agricultural basins. Three machine-learning techniques: regression trees, Gaussian processes, and support vector machine regression were employed to model daily pan evaporation rates at two meteorological stations in Kuwait. Various meteorological variables, including average diurnal temperature, average wind speed, and average relative humidity, were utilized to formulate different modeling scenarios. The three modeling methods demonstrated robust efficiency in simulating historical pan evaporation under varied input formulations. In addition, the data-driven models were shown to outperform physically and statistically based conventional evaporation modeling methods. The mean absolute error (MAE) and coefficient of determination (R2) ranged from 2.04 to 2.84 mm/day and 0.73-0.85, respectively. Notably, a bias in model predictions was observed for daily pan evaporation rates exceeding 25 mm/day. A probabilistic assessment of model skill for operational forecasts on a weekly time scale affirmed the suitability of the selected data-driven models for operational and water management decision-making. This study sought to equip water managers in arid regions with powerful tools to formulate resilient water strategies mitigating the detrimental effects of water scarcity.
英文关键词	Pan evaporation Support vector machine Gaussian processes Regression trees Arid climate Machine learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001291950500010
WOS类目	Engineering, Civil ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404539
推荐引用方式 GB/T 7714	Alsumaiei, Abdullah A.. Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models[J],2024,29(5).
APA	Alsumaiei, Abdullah A..(2024).Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models.JOURNAL OF HYDROLOGIC ENGINEERING,29(5).
MLA	Alsumaiei, Abdullah A.."Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models".JOURNAL OF HYDROLOGIC ENGINEERING 29.5(2024).

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