Appendix A. Physiologica Raw Data; Appendix B

UNSTRUCTURED The recent article published on December 23 27 in 2020 is well-written and of interest, but remains several questions that are required for clarifications, including (1) 30 feature variables with normalized format(mean=0 and SD=1) required to compare model accuracy with those with the raw-data format; (2)inconsistency in variable numbers between entry and preview panels in Figure 4 and reference typos; and (3) data-entry format with raw blood laboratory results in Figure 4 inconsistent with the model designed using normalized data to estimate parameters. We conducted a study using the training and testing data provided by the previous study. An artificial neural network(ANN) model was performed to estimate parameters and compare the model accuracy with those eight models provided by the previous study. We found that (1) normalized data yield higher accuracy than that with the raw data; (2) typos definitely exist at the bottom review (=32>30 variables in the entry) panels in Figure 4 and typos in Table 6; and (3)the ANN earns a probability of survival(=0.91) higher than that(=0.71) in the previous study using the similar entry data when the raw data are assumed in the app. We also demonstrated an author-made app using the visualization to display the prediction result, which is novel and innovative to make the result improved with a dashboard in comparison with the previous study.

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HS Cal software for the calibration of the Hargreaves-Samani equation

Pesquisa Agropecuária Brasileira ◽

10.1590/s1678-3921.pab2019.v54.00226 ◽

2019 ◽

Vol 54 ◽

Author(s):

Lucas Borges Ferreira ◽

Fernando França da Cunha ◽

Gilberto Chohaku Sediyama ◽

Flavio Bastos Campos

Keyword(s):

Time Scales ◽

Raw Data

Abstract: The objective of this work was to develop a software (HS Cal) to calibrate the Hargreaves-Samani equation for specific periods of the year, in global, seasonal, and monthly time scales, as well as to compare the performance of calibrated and non-calibrated equations. The software was developed in order to use raw data provided by Banco de Dados Meteorológicos para Ensino e Pesquisa (BDMEP). All calibrations outperformed the original Hargreaves-Samani equation, especially with the use of seasonal and monthly calibrations. The HS Cal software can easily calibrate the Hargreaves-Samani equation, allowing the identification of the best form of this equation for a given site.

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