Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions

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Abdel-Hameed, Amal MohamedDepartment of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt (författare)

Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions

Artikel/kapitelEngelska2024

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2024
Springer Nature,2024
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:ltu-104949
https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-104949URI
https://doi.org/10.1007/s11540-024-09716-1DOI

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Språk:engelska
Sammanfattning på:engelska

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Full text: CC BY License
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.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Samhällsbyggnadsteknik Vattenteknik hsv//swe
ENGINEERING AND TECHNOLOGY Civil Engineering Water Engineering hsv//eng
TEKNIK OCH TEKNOLOGIER Samhällsbyggnadsteknik Geoteknik hsv//swe
ENGINEERING AND TECHNOLOGY Civil Engineering Geotechnical Engineering hsv//eng
Artifcial neural network
Blue water footprint
Random forest
Support vector regression
Water management
Geoteknik
Soil Mechanics

Biuppslag (personer, institutioner, konferenser, titlar ...)

Abuarab, MohamedDepartment of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt (författare)
Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (författare)
Sayed, HazemIrrigation and Drainage Department, Agricultural Engineering Research Institute, Giza, 12613, Egypt (författare)
Kassem, Mohamed A.Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt (författare)
Elbeltagi, AhmedAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt (författare)
Mokhtar, AliDepartment of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt; School of Geographic Sciences Key Lab. of Geographic Information Science (Ministry of Education), East China Normal University, Zhongshan, China (författare)
Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, EgyptGeoteknologi (creator_code:org_t)

Sammanhörande titlar

Ingår i:Potato Research: Springer Nature0014-30651871-4528

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Av författaren/redakt...: Abdel-Hameed, Am ...; Abuarab, Mohamed; Al-Ansari, Nadhi ...; Sayed, Hazem; Kassem, Mohamed ...; Elbeltagi, Ahmed; visa fler...; Mokhtar, Ali; visa färre...

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