Sökning: onr:"swepub:oai:DiVA.org:ltu-104949" > Estimation of Potat...
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000 | 04944naa a2200505 4500 | |
001 | oai:DiVA.org:ltu-104949 | |
003 | SwePub | |
008 | 240403s2024 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-1049492 URI |
024 | 7 | a https://doi.org/10.1007/s11540-024-09716-12 DOI |
040 | a (SwePub)ltu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Abdel-Hameed, Amal Mohamedu Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt4 aut |
245 | 1 0 | a Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions |
264 | c 2024 | |
264 | 1 | b Springer Nature,c 2024 |
338 | a electronic2 rdacarrier | |
500 | a Full text: CC BY License | |
520 | a 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. | |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Vattenteknik0 (SwePub)201072 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Water Engineering0 (SwePub)201072 hsv//eng |
650 | 7 | a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Geoteknik0 (SwePub)201062 hsv//swe |
650 | 7 | a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Geotechnical Engineering0 (SwePub)201062 hsv//eng |
653 | a Artifcial neural network | |
653 | a Blue water footprint | |
653 | a Random forest | |
653 | a Support vector regression | |
653 | a Water management | |
653 | a Geoteknik | |
653 | a Soil Mechanics | |
700 | 1 | a Abuarab, Mohamedu Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt4 aut |
700 | 1 | a Al-Ansari, Nadhir,d 1947-u Luleå tekniska universitet,Geoteknologi4 aut0 (Swepub:ltu)nadhir |
700 | 1 | a Sayed, Hazemu Irrigation and Drainage Department, Agricultural Engineering Research Institute, Giza, 12613, Egypt4 aut |
700 | 1 | a Kassem, Mohamed A.u Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egypt4 aut |
700 | 1 | a Elbeltagi, Ahmedu Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt4 aut |
700 | 1 | a Mokhtar, Aliu Department 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, China4 aut |
710 | 2 | a Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza, 12613, Egyptb Geoteknologi4 org |
773 | 0 | t Potato Researchd : Springer Naturex 0014-3065x 1871-4528 |
856 | 4 | u https://doi.org/10.1007/s11540-024-09716-1y Fulltext |
856 | 4 | u https://ltu.diva-portal.org/smash/get/diva2:1848312/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-104949 |
856 | 4 8 | u https://doi.org/10.1007/s11540-024-09716-1 |
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