Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt

• Published in: Agricultural water management Vol. 255; p. 107052
• Main Authors: Elbeltagi, Ahmed, Azad, Nasrin, Arshad, Arfan, Mohammed, Safwan, Mokhtar, Ali, Pande, Chaitanya, Etedali, Hadi Ramezani, Bhat, Shakeel Ahmad, Islam, Abu Reza Md. Towfiqul, Deng, Jinsong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: Blue water footprint; CROPWAT; Gaussian processes; Machine learning; Maize; PUK kernel; PUK kernel; Maize; Blue water footprint; CROPWAT; Machine learning; Gaussian processes
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2021.107052

Timely and reliable water footprint prediction is imperative and prerequisite to mitigate climate risk and ensure water and food security and enhance the water-use efficiency. This study aims to model the Water Footprint (WF) by using the four kernels of Gaussian processes models (Polynomial, Normalized Poly, Radial Basis Function RBF, and Pearson Universal Function PUK) and select the best kernel with best climate scenario. This study investigates the predicting WF of maize based on meteorological variables including maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (SR), wind speed (WS), and vapor pressure deficit (VPD), Extraterrestrial radiation (Ra) relative humidity (RH) and crop coefficient (Kc) by applying ten scenarios of climate variables in the Egyptian Nile Delta, Ad Daqahliyah Governorate for predicting blue WF of maize during 2000–2019. The main findings are following as, firstly; based on developing four kernels, the performance of the PUK kernel in predicting blue WF is far better than the other three kernels followed by the Poly kernel. Secondly; for PUK kernel, model 7 (Tmax, Tmin, Tmean, WS, Sunshine Hours (SH), VPD and SR) has good performance which is close to models 8 (model 7 +Ra), model 9 (model 7 +Ra and RH) and model 10 (all inputs). Thirdly; in all four kernels, the error rate in small blue WF values is higher than the other values, moreover, the error value decreases at the medium blue WF values, while, it increases again at large WF values. Therefore, the developed models in this study can help and promote the decision makers to manage and secure the water resources management under the extreme climate events. [Display omitted] •Four GPR kernels models were developed for modeling blue WF.•Ten scenarios based on variable elimination technique were developed as inputs to GPR models.•GPRPUK Model is better than the other three models in predicting blue WF followed by Poly-kernel.•Statistical errors have decreased and correlation index has increased by increasing number of meteorological variables.