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Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt

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  • Elbeltagi, Ahmed
  • Deng, Jinsong
  • Wang, Ke
  • Hong, Yang

Abstract

Modeling crop water use as a useful decision tool for agricultural policy decision-makers to improve water-use efficiency has been increasing. This study aimed to estimate, forecast, and model the green and blue water footprints (WFg and WFb, respectively) of maize by using an artificial neural network (ANN). Three Egyptian Nile Delta governorates were selected as major maize-producing sites: Ad Daqahliyah, Al Gharbiyah, and Ash Sharqiyah. The monthly data of minimum temperature (Tmin), maximum temperature (Tmax), precipitation (P), solar radiation (SR), soil moisture (SM), wind speed (WS), and vapor pressure deficit (VPD) data were obtained from open access data over the period from 2006 to 2016. The analyzed data were divided into two parts from 2006 to 2012 and from 2013 to 2016 for model training and testing, respectively. To predict WFb in the three governorates, the results show that the models with SR, humidity (H) and VPD; Tmean, crop coefficient (Kc), and H; and SM, WS, VPD, and Kc were the best ANNs with different hidden layers (5, 3), (2, 6) and (7, 3), respectively. Furthermore, the findings showed that the optimal ANN for forecasting WFg included Tmean, WS, and P; P, WS, VPD, and SR; WS, Tmax, and VPD with hidden neuron layers (7, 3), (7, 5) and (8, 5), respectively, for the three locations. The calculated WF values achieved a high statistical significant versus those simulated in the three sites with the lowest distributional variations, and the accuracy and coefficients of determination were close to 1. Moreover, for model testing, the findings indicated that the deviations between the actual and predicted WFs ranged from - 2.6 to 6.63 % and from - 2.4 to 3.16 % for the blue and green WFs, respectively. Thus, the developed models generated relatively better results and can help promote the decision-making process for both water managers and development planners.

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  • 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).
    • Handle: RePEc:eee:agiwat:v:235:y:2020:i:c:s0378377419323868
    DOI: 10.1016/j.agwat.2020.106080
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    2. Samaa Mohy & Khadija El Aasar & Yasmin Sakr, 2023. "Decomposition Analysis of Virtual Water Outflows for Major Egyptian Exporting Crops to the European Union," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    3. 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).
    4. 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.
    5. 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).
    6. Marcelo Werneck Barbosa & José M. Cansino, 2022. "A Water Footprint Management Construct in Agri-Food Supply Chains: A Content Validity Analysis," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    7. 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).
    8. Gao, Jie & Xie, Pengxuan & Zhuo, La & Shang, Kehui & Ji, Xiangxiang & Wu, Pute, 2021. "Water footprints of irrigated crop production and meteorological driving factors at multiple temporal scales," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).

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