干旱区生态环境知识资源中心(Arid): Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration

Arid

DOI	10.1371/journal.pone.0217520
	Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration
	Wu, Lifeng 1; Fan, Junliang 2
通讯作者	Fan, Junliang
来源期刊	PLOS ONE
ISSN	1932-6203
出版年	2019
卷号	14 期号:5
英文摘要	Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001-2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R-2 = 0.829, RMSE = 0.718 mm day(-1), NRMSE = 0.250 and MAE = 0.508 mm day(-1)). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub) tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models.
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold, Green Submitted, Green Published
收录类别	SCI-E
WOS记录号	WOS:000469759100062
WOS关键词	SUPPORT-VECTOR-MACHINE ; GLOBAL SOLAR-RADIATION ; LIMITED CLIMATIC DATA ; ARPS DECLINE MODEL ; EMPIRICAL EQUATIONS ; REGRESSION ; NETWORK ; ANFIS ; TEMPERATURE ; SVM
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
来源机构	西北农林科技大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218109
作者单位	1.Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang, Jiangxi, Peoples R China; 2.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Wu, Lifeng,Fan, Junliang. Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration[J]. 西北农林科技大学,2019,14(5).
APA	Wu, Lifeng,&Fan, Junliang.(2019).Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.PLOS ONE,14(5).
MLA	Wu, Lifeng,et al."Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration".PLOS ONE 14.5(2019).