气候变化领域集成服务门户(CCPortal): Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change

CCPortal

DOI	10.1080/23311932.2024.2348697
	Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change
	Youssef, Mohamed A.; Peters, R. Troy; El-Shirbeny, Mohammed; Abd-ElGawad, Ahmed M.; Rashad, Younes M.; Hafez, Mohamed; Arafa, Yasser
发表日期	2024
ISSN	2331-1932
起始页码	10
结束页码	1
卷号	10 期号:1
英文摘要	This study addressed the increasing challenges of climate change by exploring the use of machine learning (ML) algorithms to predict the reference evapotranspiration (ETo). Accurate ETo prediction is crucial for optimizing irrigation water management. This research aimed to assess the reliability and accuracy of ML algorithms in predicting ETo values. Three ETo calculation methods were employed: Penman-Monteith (PM), Hargreaves (HA), and Blaney-Criddle (BC). The study analyzed ETo and other climate variables using the modified Mann-Kendall test (m-MK) and Theil Sen's slope estimator methods to identify trends. Multiple ML algorithms, including Support Vector Regression (SVR), Random Forest (RF), XGboost, K-Nearest Neighbor (KNN), Decision Trees (DT), Linear Regression (LR), and Multiple Linear Regression (MLR) were utilized for ETo prediction. The ML algorithms exhibited excellent performance, with coefficients of determination (R-2) values ranging from 0.97 to 0.99 for PM, 0.99 for HA, and from 0.91 to 0.92 for BC. The models demonstrated a high value of the Kling-Gupta efficiency (KGE) with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. Strong correlations between the predicted and calculated daily ETo were observed with R-2 values of 0.99, 0.99, and 0.92 for PM, HA, and BC methods, respectively. In conclusion, this study affirmed the accuracy and reliability of ML algorithms to match that of standard ETo prediction equations.
英文关键词	Climate change; reference evapotranspiration; machine learning algorithms; modified Mann-Kendall test; Kling-Gupta efficiency
语种	英语
WOS研究方向	Agriculture
WOS类目	Agriculture, Multidisciplinary
WOS记录号	WOS:001217415900001
来源期刊	COGENT FOOD & AGRICULTURE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/309910
作者单位	Egyptian Knowledge Bank (EKB); Ain Shams University; Washington State University; Egyptian Knowledge Bank (EKB); National Authority for Remote Sensing & Space Sciences (NARSS); King Saud University; Egyptian Knowledge Bank (EKB); City of Scientific Research & Technological Applications (SRTA-City); Egyptian Knowledge Bank (EKB); City of Scientific Research & Technological Applications (SRTA-City)
推荐引用方式 GB/T 7714	Youssef, Mohamed A.,Peters, R. Troy,El-Shirbeny, Mohammed,et al. Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change[J],2024,10(1).
APA	Youssef, Mohamed A..,Peters, R. Troy.,El-Shirbeny, Mohammed.,Abd-ElGawad, Ahmed M..,Rashad, Younes M..,...&Arafa, Yasser.(2024).Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change.COGENT FOOD & AGRICULTURE,10(1).
MLA	Youssef, Mohamed A.,et al."Enhancing irrigation water management based on ETo prediction using machine learning to mitigate climate change".COGENT FOOD & AGRICULTURE 10.1(2024).