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

Author

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  • 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

Abstract

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.

Suggested Citation

  • 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, 2021. "Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt," Agricultural Water Management, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:agiwat:v:255:y:2021:i:c:s0378377421003176
    DOI: 10.1016/j.agwat.2021.107052
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    1. Ahmadi, Mojgan & Etedali, Hadi Ramezani & Elbeltagi, Ahmed, 2021. "Evaluation of the effect of climate change on maize water footprint under RCPs scenarios in Qazvin plain, Iran," Agricultural Water Management, Elsevier, vol. 254(C).
    2. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Hong, Yang, 2020. "Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt," Agricultural Water Management, Elsevier, vol. 235(C).
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    6. Rana Muhammad Adnan & Abolfazl Jaafari & Aadhityaa Mohanavelu & Ozgur Kisi & Ahmed Elbeltagi, 2021. "Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
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    10. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
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    1. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).
    2. Abhinav Kumar Singh & Pankaj Kumar & Rawshan Ali & Nadhir Al-Ansari & Dinesh Kumar Vishwakarma & Kuldeep Singh Kushwaha & Kanhu Charan Panda & Atish Sagar & Ehsan Mirzania & Ahmed Elbeltagi & Alban Ku, 2022. "An Integrated Statistical-Machine Learning Approach for Runoff Prediction," Sustainability, MDPI, vol. 14(13), pages 1-30, July.
    3. Gerkani Nezhad Moshizi, Zahra & Bazrafshan, Ommolbanin & Ramezani Etedali, Hadi & Esmaeilpour, Yahya & Collins, Brain, 2023. "Application of inclusive multiple model for the prediction of saffron water footprint," Agricultural Water Management, Elsevier, vol. 277(C).
    4. Parveen Sihag & Meysam Nouri & Hedieh Ahmadpari & Amin Seyedzadeh & Ozgur Kisi, 2022. "Approximation of the Discharge Coefficient of Radial Gates Using Metaheuristic Regression Approaches," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    5. Mahmood Ahmad & Suraparb Keawsawasvong & Mohd Rasdan Bin Ibrahim & Muhammad Waseem & Kazem Reza Kashyzadeh & Mohanad Muayad Sabri Sabri, 2022. "Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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