The 2006 turbidity prediction model was selected as the best predictive model because of its ability to predict turbidity within the acceptable error range and required confidence interval.
Test results demonstrate that the 2006 turbidity prediction model was selected as the best predictive model (RMSE = 5.787 and CC = 0.975) because of its ability to predict turbidity within the acceptable error range and 90%% required confidence interval (50NTU).
Finally, based on the best feature combination, we developed one best predictive model set and applied it to predict the primary tumor sites.
Then, stepwise linear regression analysis was performed to identify the best predictive model using important parameters.
The best predictive model is a 17-layered DNN architecture (excluding four dropout layers) with tuned hyperparameters; we refer to this model as ElemNet.
The red lines represent the best averaged R2 values used to select the best predictive model for each continent (Supplementary Table 3) while the red dots represent the R2 values, for all withheld countries, calculated using the best predictive model referring to the continent in which they are located.
The quadratic model was found to be the best predictive model for describing the cooking behavior of rice.
The total number of peaks yielded the best predictive model and is used for optimization of parameters setting.
The best predictive model was applied to the airborne AISA-EAGLE hyperspectral data to model the phenological macro-stages of barley at the landscape level.
The best predictive model developed for runoff 1 month ahead in its input structure comprises of historical time series of runoff, rainfall and temperature which demonstrates the influence of the thermal characteristics in mountainous regions.
A network with 10 neurons and a tansig transfer function in both the input and output layers was found to be the best predictive model of all of the models evaluated.
