Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data

Reis, Matheus Mendes; Silva, Ariovaldo José da; Zullo, Jurandir; Santos, L.D. Tuffi; Azevedo, Alcinei Místico; Lopes, Érika Manuela Gonçalves

doi:10.1016/j.compag.2019.104937

Cited by 55 publications

(33 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study showed that, even in the case of two parameters (temperature and radiation), the MLP still outperformed Makkink model and HS model while having comparable performance with the PT model. Some other studies reported by other literature also showed that the MLP could give better estimation than equivalent conventional empirical models in the case of limited climatic parameters, in the four main classes of climate regions such as semi-arid [50,51], arid [31,52,53], humid, and semi humid regions [54].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 76%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

View full text Add to dashboard Cite

Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.

show abstract

Section: Artificial Neural Network (Ann)mentioning

confidence: 76%

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Oddly, stations 83338 and 83388 did not improve the performance for any approach, even decreasing performance for approaches A1 and A2, while station 83395 only slightly improved its performance. In fact, Reis et al [25] also assessed the HS model for these same three stations and observed no improvements in correlation between observed and predicted values, even when performing the HS model calibration station by station.…”

Section: Performances Of Improved Hargreaves-samani Modelsmentioning

confidence: 97%

Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration

Althoff

Santos

Bazame

et al. 2019

Water

View full text Add to dashboard Cite

Improving irrigation water management is an important asset when facing increased water shortages. The Hargreaves–Samani (HS) method is a simple method that can be used as an alternative to the Penman–Monteith (PM) method, which requires only temperature measurements for estimating reference evapotranspiration (ETo). However, the applicability of this method relies on its calibration to local meteorological specificities. The objective of this study was to investigate the effects of local calibration on the performance of the HS equation. The study was carried out for the middle portion of the São Francisco River Basin (MSFB), Brazil, and considered four calibration approaches: A1—single calibration for the entire MSFB; A2—separate calibration by clusters of months; A3—by clusters of stations; and A4—for all contexts resulting by combining A2 and A3. Months from the wet season showed larger improvements by the calibration of the HS model, since mean air temperature and its daily range showed stronger correlations to ETo. On the other hand, the months from the dry season and stations from the eastern region of MSFB performed poorly regardless of the calibration approach adopted. This occurred because, in those cases, ETo presented larger correlation to variables that are missing in the HS equation, and the use of the full PM equation seems unavoidable.

show abstract

“…They found that the ANN trained by the Levenberg-Marquardt algorithm with 9 neurons in a single hidden layer made the best estimation performance in their case. The ANNs with multiple linear regression (MLR), ELM, and HS models were tested by Reis et al (2019) to predict ET 0 using temperature data in the Verde Grande River basin, Brazil. The study revealed that AI methods have superior performance over other models.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

Sattari

Apaydín

Band

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kernel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden–Fletcher–Goldfarb–Shanno artificial neural network (BFGS-ANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R2=0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R2=0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.

show abstract

Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data

Cited by 55 publications

References 35 publications

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration

Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

Contact Info

Product

Resources

About