Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment

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Elbeltagi, AhmedAgricultural Engineering Dept., Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt (författare)

Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment

Artikel/kapitelEngelska2023

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2022-12-30
Springer,2023
electronicrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:ltu-95300
https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-95300URI
https://doi.org/10.1007/s13201-022-01846-6DOI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:art swepub-publicationtype

Anmärkningar

Validerad;2023;Nivå 2;2023-02-02 (sofila)
Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces characterized by nonlinearity, non-stationary, and stochasticity. Such factors clearly hinder setting up rigorous predictive models. This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms—random Subspace (RSS), M5 pruned (M5P), reduced error pruning tree (REPTree), and bagging for estimating pan evaporation rates. Meteorological data encompass maximum temperature, minimum temperature, mean temperature, relative humidity, and wind speed from three different agroclimatic stations in Iraq (i.e., Baghdad, Mosul, and Basrah) were utilized as predictor parameters. The regression model in addition to the sensitivity analysis was employed to identify the best-input combinations for the evaluated methods. It was demonstrated that the AR-M5P estimated the evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The AR-M5P model provided the best performance indicators, i.e., MAE = 33.82, RMSE = 45.05, RAE = 24.75, RRSE = 28.50, and r = 0.972 for Baghdad; MAE = 25.82, RMSE = 35.95, RAE = 23.75, RRSE = 29.64, and r = 0.956 for Mosul station, respectively. The outcomes of this study proved the superior performance of the hybridized methods in addressing such intricate hydrological relationships and hence could be employed for other environmental problems.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Samhällsbyggnadsteknik Geoteknik hsv//swe
ENGINEERING AND TECHNOLOGY Civil Engineering Geotechnical Engineering hsv//eng
Additive regression
Machine learning
Pan evaporation
REPTree
Soil Mechanics
Geoteknik

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

Al-Mukhtar, MustafaCivil Engineering Department, University of Technology, Baghdad, Iraq (författare)
Kushwaha, N. L.Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India (författare)
Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (författare)
Vishwakarma, Dinesh KumarDepartment of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India (författare)
Agricultural Engineering Dept., Faculty of Agriculture, Mansoura University, Mansoura, 35516, EgyptCivil Engineering Department, University of Technology, Baghdad, Iraq (creator_code:org_t)

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Ingår i:Applied water science: Springer13:22190-54872190-5495

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Av författaren/redakt...: Elbeltagi, Ahmed; Al-Mukhtar, Must ...; Kushwaha, N. L.; Al-Ansari, Nadhi ...; Vishwakarma, Din ...

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