干旱区生态环境知识资源中心(Arid): Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region

Arid

DOI	10.1007/s11269-023-03443-x
	Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region
	Mokhtar, Ali; Al-Ansari, Nadhir; El-Ssawy, Wessam; Graf, Renata; Aghelpour, Pouya; He, Hongming; Hafez, Salma M.; Abuarab, Mohamed
通讯作者	Abuarab, M
来源期刊	WATER RESOURCES MANAGEMENT
ISSN	0920-4741
EISSN	1573-1650
出版年	2023
卷号	37 期号:4 页码:1557-1580
英文摘要	Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions.
英文关键词	Water resources management Climate change Evapotranspiration Hybrid models Long short-term memory
类型	Article
语种	英语
开放获取类型	hybrid, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000946825800002
WOS关键词	SUPPORT VECTOR MACHINE ; SOLAR-RADIATION MODELS ; NEURAL-NETWORKS ; REFERENCE EVAPOTRANSPIRATION ; RANDOM FOREST ; CROP ; TEMPERATURE ; PERFORMANCE ; VALIDATION ; STRATEGIES
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399136
推荐引用方式 GB/T 7714	Mokhtar, Ali,Al-Ansari, Nadhir,El-Ssawy, Wessam,et al. Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region[J],2023,37(4):1557-1580.
APA	Mokhtar, Ali.,Al-Ansari, Nadhir.,El-Ssawy, Wessam.,Graf, Renata.,Aghelpour, Pouya.,...&Abuarab, Mohamed.(2023).Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region.WATER RESOURCES MANAGEMENT,37(4),1557-1580.
MLA	Mokhtar, Ali,et al."Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region".WATER RESOURCES MANAGEMENT 37.4(2023):1557-1580.