Desempenho de Métodos de Preenchimento de Falhas em Dados de Evapotranspiração de Referência para Região Oeste do Paraná

Giovanella, Tharsos Hister; Oliveira, Fabrício Correia de; Marchi, Vitor; Tluszcz, Júlia

doi:10.1590/0102-77863630001

Cited by 4 publications

(7 citation statements)

References 21 publications

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“…It is necessary to note that the filling possibilities extend beyond the ones employed here. Other methodologies include Splines interpolation, artificial neural networks, the natural neighbor method, kriging, the least squares method, and regional weighting by correlation, among others (Brubacher et al, 2020;Giovanella et al, 2021;Rahman et al, 2023;Ruezzene et al, 2020). Regarding the method investigated in this study, the suitability of ETO estimation aligns with findings from other researchers (Giovanella et al, 2021;Mardikis et al, 2005;Rahman et al, 2023).…”

Section: Resultssupporting

confidence: 71%

“…Multiple Linear Regression (MLR) (Equation 8), and Virtual Station data substitution (VS) (Bier & Ferraz, 2017;Brubacher et al, 2020;Giovanella et al, 2021).…”

Section: Data Gap Filling Methodsmentioning

confidence: 99%

“…For the calculation of ET0, various approaches can be employed when data is incomplete. Options include filling each model input, assuming a constant wind speed of 2 m/s, estimating solar radiation from maximum and minimum temperatures, or selectively filling only the model output based on estimation results from neighboring stations (Allen et al, 1998;Conceição, 2006;Zotarelli et al, 2010;Giovanella et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…It's worth noting that most studies on data filling primarily concentrate on directly measured station data, such as temperature and precipitation, rather than estimated data like ET (Giovanella et al, 2021). Filling gaps directly in the model output can signify a significant advancement in terms of data processing agility, contingent upon the accuracy of the filling methodology.…”

Section: Introductionmentioning

confidence: 99%

“…RMSE and MAE can assume any positive value, and since they represent the difference between observed and estimated values, the closer to zero they are, the smaller their difference will be. CE and r can be classified as suggested by Giovanella et al (2021) (Table 1). The input variables may vary based on the chosen method and user data availability.…”

mentioning

confidence: 99%

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Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

Corrêa,

Silva,

Scariot

et al. 2024

Preprint

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Despite the several existing initiatives to obtain evapotranspiration data for several scientific analysis, it remains a challenge when dealing with data scarcity, especially when it is coupled with many data gaps of long-time climatological data series. In this study, we developed two Python scripts to perform reference evapotranspiration calculations in data scarcity conditions, filling potential data gaps, and testing them in the Urucuia River watershed.The first script (ETO_calc.py) is able to calculate evapotranspiration both by the Penman-Monteith method, adapting it according to user data availability, and by the Hargreaves-Samani method. The second script (Filling_gaps.py) fills out potential data gaps based on input from neighboring stations and tests the adequacy of the filling. The first script was tested by comparing the data generated by it with the online calculator 'ETCalc', while the second was tested by filling in a station's data from five neighboring stations, comparing the original data with the filled data. The output of the first script was practically identical to the online calculator used for comparison, which confirms the adequacy of the estimation made by ETO_calc.py. As for the second script, the regional weighting method and both linear and multiple regression methods were the methods that best filled in the missing data for the main station. This is due to their better fit to the historical variation of the data compared to the other tested methods. The results allow us to conclude that both scripts proved to be suitable tools for estimating evapotranspiration data.

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Section: Resultssupporting

confidence: 71%

“…Multiple Linear Regression (MLR) (Equation 8), and Virtual Station data substitution (VS) (Bier & Ferraz, 2017;Brubacher et al, 2020;Giovanella et al, 2021).…”

Section: Data Gap Filling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

Corrêa,

Silva,

Scariot

et al. 2024

Preprint

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Paradox between adequate sanitation and rainfall in dengue fever cases

Oliveira

Murari

Filho

et al. 2023

Science of The Total Environment

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Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series

Coutinho,

Madeira,

Silva

et al. 2023

Rev. bras. meteorol.

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The present study applies computational intelligence techniques in the development of a hybrid model composed of Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) (MLP-GA) to estimate and fill in the gaps in the monthly variables of evaporation, maximum temperature and relative humidity to six regions in the state of Rio de Janeiro (RJ), Brazil. The results were evaluated using statistical techniques and compared with results obtained by the Multiple Linear Regression (RLM), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models and also compared with the data recorded by the weather stations. The correlation coefficient (r) between the evaporation estimates generated by MLP-GA with the recorded data showed a high relationship, remaining between 0.82 to 0.97. The average percentage error (MPE) ranged from 6.01% to 9.67%, indicating a accuracy between 90% to 94%. For the maximum temperature generated by MLP-GA the correlation with the recorded data remained between 0.97 to 0.99. It also presented the MPE between 0.95% to 1.57%, maintaining the accuracy of the estimated data between 98% to 99%. The correlation coefficient (r) between the relative humidity estimates generated with the MLP-GA remained between 0.89 a 0.97, the MPE between 1.15% to 1.89%, which guaranteed a rate higher than 98% of correctness in its estimates. Such results demonstrated gains in relation to the other applied models and allowed the accomplishment of the filling of most of the missing values.

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Desempenho de Métodos de Preenchimento de Falhas em Dados de Evapotranspiração de Referência para Região Oeste do Paraná

Cited by 4 publications

References 21 publications

Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

Reference Evapotranspiration for Sparse Data and Data Gap Filling in Python: A Study Case at Urucuia River Basin, Brazil

Paradox between adequate sanitation and rainfall in dengue fever cases

Application of a Computational Hybrid Model to Estimate and Filling Gaps for Meteorological Time Series

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