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

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Sökning: WFRF:(Elbeltagi Ahmed) > Estimation of Potat...

LIBRIS Formathandbok (Information om MARC21)

Fältnamn	Indikatorer	Metadata
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

Hitta via bibliotek

Potato Research (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

Hitta mer i SwePub

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

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Samhällsbyggnads ...; och Vattenteknik

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Samhällsbyggnads ...; och Geoteknik

Artiklar i publikationen: Potato Research

Av lärosätet: Luleå tekniska universitet

Sök utanför SwePub

Sök vidare i:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se