(a) Four kinds of neural network models, namely, back-propagation neural network, dynamic architecture for artificial neural network, Elman artificial neural network, and echo state network, are selected as component forecasting models.
Therefore, NNM5 selected as the best neural network for forecasting the percentage, velocity and coefficient of water absorption.
A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model.
Following a detailed study of the commissioning process and its criticalities, Hazop is selected as the most suitable approach in forecasting particular risks of this project stage.
For the application scenario of flood monitoring, the flood forecasting task is selected as an example to test the versatility of the proposed node meta-model and the applicability of the formal expression based on the nine-tuple metadata framework.
Therefore, according to the teleconnection analysis, forecasting errors from WFe and WFf are selected as the potential input variables to improve the forecasts of WFa.
The forecast horizon is selected as 2, 10, and 40 days ahead to evaluate the models' performances in longer horizons.
Accordingly, the fields to be selected as the targets will affect the final forecast for any given target topic.
The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting.
The forecast model for t is selected as the best performing model up to t − 1. Performance is judged based on the predictive log score (i.e., the forecast log-likelihood)[27].
