Reference health
Prediction of actual evapotranspiration by artificial neural network models using data from a Bowen ratio energy balance station
2 of 50 checkable references need attention · checked 2026-07-23
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
13 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
References needing attention
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does not resolve to a known work10.1561/2400000003a
The 48 checked references that resolve
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Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran
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Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables
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Lysimetric Calibration of the Bowen Ratio-Energy Balance Method for Evapotranspiration Estimation in the Central Great Plains
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The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets
resolves10.1139/L09-074
Reference evapotranspiration forecasting using different artificial neural networks algorithms
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Bowen Ratio estimates of evapotranspiration for <i>Tamarix ramosissima</i> stands on the Virgin River in southern Nevada
resolves10.1016/j.biosystemseng.2012.11.001
Examination of the Bowen ratio energy balance technique for evapotranspiration estimates in screenhouses
resolves10.1029/98WR00556
Effect of removal of <i>Juniperus ashei</i> on evapotranspiration and runoff in the Seco Creek Watershed
resolves10.1080/02626666509493388
ACCURACY OF EVAPOTRANSPIRATION DETERMINATIONS BY THE BOWEN RATIO METHOD
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Accuracy of the Bowen ratio-energy balance method for measuring latent heat flux in a semiarid advective environment
resolves10.1038/35016072
Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
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Fetch requirements for bowen ratio measurements of latent and sensible heat fluxes
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Artificial neural network models for reference evapotranspiration in an arid area of northwest China
resolves10.1002/hyp.6819
Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation
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Generalized regression neural networks for evapotranspiration modelling
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Comparative study of conventional and artificial neural network-based ETo estimation models
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Development and Validation of GANN Model for Evapotranspiration Estimation
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Estimating Evapotranspiration using Artificial Neural Network
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Artificial neural networks approach in evapotranspiration modeling: a review
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Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions
resolves10.1016/j.agwat.2007.12.011
Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)
resolves10.1016/0022-1694(93)90276-F
Comparison of the Bowen ratio-energy balance and the water balance methods for the measurement of evapotranspiration
resolves10.1007/s00521-018-3410-8
GEP and MLR approaches for the prediction of reference evapotranspiration
resolves10.2134/agronmonogr47.c16
Current Micrometeorological Flux Methodologies with Applications in Agriculture
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Trends in land surface evapotranspiration across China with remotely sensed NDVI and climatological data for 1981–2010
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Objective Criteria for Rejecting Data for Bowen Ratio Flux Calculations
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ESTIMATION OF REFERENCE EVAPOTRANSPIRATION IN THE MAULE REGION OF CHILE: A COMPARISON BETWEEN THE FAO PENMAN-MONTEITH AND BOWEN RATIO METHODS
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Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India
resolves10.1016/j.agwat.2005.06.001
Comparison of different methods to measure and model actual evapotranspiration rates for a wet sloping grassland
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Assessment of reliability of Bowen ratio method for partitioning fluxes
resolves10.1029/2008JD010174
Comparison of methods for estimating evapotranspiration rate of dry forest canopy: Eddy covariance, Bowen ratio energy balance, and Penman‐Monteith equation
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Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique
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Multilayer perceptron for reference evapotranspiration estimation in a semiarid region
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Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network
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The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment
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Forecasting of Reference Evapotranspiration by Artificial Neural Networks
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A model of evapotranspiration based on the theory of maximum entropy production
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Validating Evapotranspiraiton Equations Using Bowen Ratio in New Brunswick, Maritime, Canada
The 13 references without a DOI — listed, not checked
no DOI — not checkedAllen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and drainage Paper No. 56, FAO, Rome
no DOI — not checkedAllen RG, Hill RW, Srikanth V (1994) Evapotranspiration parameters for variably sized wetlands. Am Soc Agric Eng 94:21–32
no DOI — not checkedAytek A, Guven A, Yuce MI, Aksoy H (2008) An explicit neural network formulation for evapotranspiration. Hydrol Sci J 54(2):382–388
no DOI — not checkedErshadi A, McCabe MF, Evans JP, Walker JP (2011) Evaluation of energy balance, combination, and complementary schemes for estimation of evaporation. IAHS-AISH Publ 344(7):52–56
no DOI — not checkedEvrendilek F (2014) Assessing neural networks with wavelet denoising and regression models in predicting diel dynamics of eddy covariance-measured latent and sensible heat fluxes, and evapotranspiration. Neural Comput Appl 23:1387–1393
no DOI — not checkedGoyal, Harmsen EW (2014) Evapotranspiration: Principles and applications for water management. Apple Academic Press, Oakville, ON, & Waretown, NJ
no DOI — not checkedHinton G, Srivastava N, Swersky K (2012) Neural networks for machine learning—Lecture 6e—rmsprop: Divide the gradient by a running average of its recent magnitude
no DOI — not checkedJones JW, Ritchie JT (1990) Crop growth models. In: Hoffman GJ, Howel TA, Solomon KH Management of farm irrigation systems. ASAE Monograph No. 9:63–89
no DOI — not checkedMinistry of the Environment, Conservation and Parks. Lake Ontario Lakewide Action and Management Plan. Annual Report 2015
no DOI — not checkedPayero JO, Neale CMU, Wright JL, Allen RG (2003) Guidelines for validating Bowen ratio data. Am Soc Agric Eng 46(4):1051–1060
no DOI — not checkedShuttleworth WJ (1993) Evaporation. In: Maidmen DR (ed) Handbook of hydrology. McGraw-Hill, New York, pp 4.1–4.53
no DOI — not checkedTanner BD, Greene JP, Bingham GE (1987) A Bowen ratio design for long term measurements. In: Proceedings of the ASAE 1987 international winter meeting, St. Joseph, Michigan
no DOI — not checkedTufféry S (2007) Data mining et statistique décisionnelle. L’intelligence de données, Modèles linéaire, Régression logistique, Réseaux de neurones, Scoring et Text mining. Edition TECHNIP, Paris
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