气候变化领域集成服务门户(CCPortal): Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?

CCPortal

DOI	10.3390/agronomy14030532
	Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?
	Yamac, Sevim Seda; Kurtulus, Bedri; Memon, Azhar M.; Alomair, Gadir; Todorovic, Mladen
发表日期	2024
EISSN	2073-4395
起始页码	14
结束页码	3
卷号	14 期号:3
英文摘要	This study examined the performance of random forest (RF), support vector machine (SVM) and adaptive boosting (AB) machine learning models used to estimate daily potato crop evapotranspiration adjusted (ETc-adj) under full irrigation (I100), 50% of full irrigation supply (I50) and rainfed cultivation (I0). Five scenarios of weather, crop and soil data availability were considered: (S1) reference evapotranspiration and precipitation, (S2) S1 and crop coefficient, (S3) S2, the fraction of total available water and root depth, (S4) S2 and total soil available water, and (S5) S3 and total soil available water. The performance of machine learning models was compared with the standard FAO56 calculation procedure. The most accurate ETc-adj estimates were observed with AB4 for I100, RF3 for I50 and AB5 for I0 with coefficients of determination (R2) of 0.992, 0.816 and 0.922, slopes of 1.004, 0.999 and 0.972, modelling efficiencies (EF) of 0.992, 0.815 and 0.917, mean absolute errors (MAE) of 0.125, 0.405 and 0.241 mm day-1, root mean square errors (RMSE) of 0.171, 0.579 and 0.359 mm day-1 and mean squared errors (MSE) of 0.029, 0.335 and 0.129 mm day-1, respectively. The AB model is suggested for ETc-adj prediction under I100 and I0 conditions, while the RF model is recommended under the I50 condition.
英文关键词	irrigation; water stress; random forest; support vector machine; adaptive boosting; machine learning
语种	英语
WOS研究方向	Agriculture ; Plant Sciences
WOS类目	Agronomy ; Plant Sciences
WOS记录号	WOS:001191750500001
来源期刊	AGRONOMY-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298325
作者单位	Necmettin Erbakan University; Mugla Sitki Kocman University; King Fahd University of Petroleum & Minerals; King Faisal University; CIHEAM; CIHEAM BARI
推荐引用方式 GB/T 7714	Yamac, Sevim Seda,Kurtulus, Bedri,Memon, Azhar M.,et al. Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?[J],2024,14(3).
APA	Yamac, Sevim Seda,Kurtulus, Bedri,Memon, Azhar M.,Alomair, Gadir,&Todorovic, Mladen.(2024).Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?.AGRONOMY-BASEL,14(3).
MLA	Yamac, Sevim Seda,et al."Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?".AGRONOMY-BASEL 14.3(2024).