Author:
Jibrin Abdulhayat M.,Al-Suwaiyan Mohammad,Aldrees Ali,Dan’azumi Salisu,Usman Jamilu,Abba Sani I.,Yassin Mohamed A.,Scholz Miklas,Sammen Saad Sh.
Publisher
Springer Science and Business Media LLC
Reference47 articles.
1. El Bilali, A. & Taleb, A. Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment. J. Saudi Soc. Agric. Sci. 19, 439–451 (2020).
2. Busico, G. et al. A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses. Water Res. 171, 115386 (2020).
3. Li, D. Quantifying water use and groundwater recharge under flood irrigation in an arid Oasis of Northwestern China. Agric. Water Manag. 240, 106326 (2020).
4. Batarseh, M. et al. Dataset for the physio-chemical Parameters of Groundwater in the Emirate of Abu Dhabi, UAE. Data Br. 38, 107353 (2021).
5. Zimit, A. Y., Jibril, M. M., Azimi, M. S. & Abba, S. I. Hybrid predictive based control of precipitation in a water-scarce region: A focus on the application of intelligent learning for green irrigation in agriculture sector. J. Saudi Soc. Agric. Sci. https://doi.org/10.1016/j.jssas.2023.06.001 (2023).