干旱区生态环境知识资源中心(Arid): Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions

Arid

DOI	10.1007/s11540-024-09716-1
	Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
	Abdel-Hameed, Amal Mohamed; Abuarab, Mohamed; Al-Ansari, Nadhir; Sayed, Hazem; Kassem, Mohamed A.; Elbeltagi, Ahmed; Mokhtar, Ali
通讯作者	Abuarab, M
来源期刊	POTATO RESEARCH
ISSN	0014-3065
EISSN	1871-4528
出版年	2024
英文摘要	Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (T max), minimum temperature (T min), average temperature (T ave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990-2016. Six scenarios (Sc1-Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
英文关键词	Artificial neural network Blue water footprint Random forest Support vector regression Water management
类型	Article ; Early Access
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001195303700001
WOS关键词	REFERENCE EVAPOTRANSPIRATION ; NILE DELTA ; CLIMATE ; RIVER ; IRRIGATION ; PREDICTION ; MANAGEMENT ; EQUATIONS ; EGYPT ; TRADE
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405217
推荐引用方式 GB/T 7714	Abdel-Hameed, Amal Mohamed,Abuarab, Mohamed,Al-Ansari, Nadhir,et al. Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions[J],2024.
APA	Abdel-Hameed, Amal Mohamed.,Abuarab, Mohamed.,Al-Ansari, Nadhir.,Sayed, Hazem.,Kassem, Mohamed A..,...&Mokhtar, Ali.(2024).Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions.POTATO RESEARCH.
MLA	Abdel-Hameed, Amal Mohamed,et al."Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions".POTATO RESEARCH (2024).