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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt.
Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt.ORCID iD: 0000-0002-8799-8031
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6790-2653
Irrigation and Drainage Department, Agricultural Engineering Research Institute, Giza, 12613, Egypt.
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2024 (English)In: Potato Research, ISSN 0014-3065, E-ISSN 1871-4528Article in journal (Refereed) Epub ahead of print
Abstract [en]

Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners. 

Place, publisher, year, edition, pages
Springer Nature, 2024.
Keywords [en]
Artifcial neural network, Blue water footprint, Random forest, Support vector regression, Water management
National Category
Water Engineering Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-104949DOI: 10.1007/s11540-024-09716-1OAI: oai:DiVA.org:ltu-104949DiVA, id: diva2:1848312
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Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2024-04-03

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Al-Ansari, Nadhir

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