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Sökning: WFRF:(Pham Quoc Bao) > (2024) > Sensitivity of dail...

Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network

Gupta, Sanjeev (författare)
Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India
Kumar, Pravendra (författare)
Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India
Kishore, Gottam (författare)
ICAR-Central Institute of Agricultural Engineering, Bhopal, Madhya Pradesh, India
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Ali, Rawshan (författare)
Civil Engineering Department, University of Raparin, Rania, Kurdistan Region, Iraq
Al-Ansari, Nadhir, 1947- (författare)
Luleå tekniska universitet,Geoteknologi
Vishwakarma, Dinesh Kumar (författare)
Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India
Kuriqi, Alban (författare)
CERIS, Instituto Superior T´Ecnico, Universidade de Lisboa, 1049-001, Lisbon, Portugal; Civil Engineering Department, University for Business and Technology, 10000, Pristina, Kosovo
Pham, Quoc Bao (författare)
Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200, Sosnowiec, Poland
Kisi, Ozgur (författare)
Faculty of Natural Sciences and Engineering, Ilia State University, 0162, Tbilisi, Georgia; Department of Civil Engineering, University of Applied Sciences, 23562, Lübeck, Germany
Heddam, Salim (författare)
Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 août 1955, Route El Hadaik, BP 26, Skikda, Algeria
Mattar, Mohamed A. (författare)
Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia
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Department of Soil and Water Conservation Engineering, GB. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India ICAR-Central Institute of Agricultural Engineering, Bhopal, Madhya Pradesh, India (creator_code:org_t)
Springer Nature, 2024
2024
Engelska.
Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:6
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Geotechnical Engineering (hsv//eng)

Nyckelord

ANN
ANFIS
FAO-56 Penman–Monteith
Sensitivity analysis
Wavelet neural network
Geoteknik
Soil Mechanics

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