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The integral pumping test (IPT) method overcomes this problem by increasing the sampled volume.
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Approximately results are consistent by increasing the sample size.
Error in sampling can be reduced by increasing the sample size.
It is simply seen that the value of error decreases by increasing the sample size.
Weaknesses in data can be effectively addressed by increasing the sample size.
Based on the figure, all methods except method 3 generally depict downward trend by increasing the sampling frequency because the estimation accuracy increases by high sampling rate.
However, as it is depicted in Fig. 4, the computational time increases by increasing the sample size.
The result suggests that predictions using RFBC can always achieve the same level of accuracy by increasing the sample size.
Random error arises as a result of variation between samples that might be drawn in a study and can be reduced by increasing the sample size.
And (3) the loss of accuracy in RMSE or R2 due to bias correction algorithms can potentially be compensated by increasing the sample size of the training data.
According to the figure, by increasing the sample number per cycle, the calculation times of C P U move up in all methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com