Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance

This research study examines the use of two models of artificial intelligence based on a single neural network (SNN) and bootstrap aggregated neural networks (BANN) for the prediction value of hourly global horizontal irradiance (GHI) received over one year in Tamanrasset City (Southern Algeria). The SNN and BANN were created using overall data points. To improve the accuracy and durability of neural network models generated with a limited amount of training data, stacked neural networks are developed. To create many subsets of training data, the training dataset is re-sampled using bootstrap re-sampling with replacement. A neural network model is created for each set of training datasets. A stacked neural network is created by combining multiple individual neural networks (INN). For the testing phase, higher correlation coefficients (R = 0.9580) were discovered when experimental global horizontal irradiance (GHI) was compared to predicted global horizontal irradiance (GHI). The performance of the models (INN, BANN, and SNN) demonstrates that models generated with BANN are more accurate and robust than models built with individual neural networks (INN) and (SNN)

Regression-Based Models for Predicting Discharge Coefficient of Triangular Side Orifice

- This study introduced another technique to predict the discharge coefficient (Cd) of the triangular side orifice (TSO). This technique is based on the SPSS software as multiple linear regression (MLR) and multiple nonlinear regression (MNLR) models. These models were established using 570 experimental datasets, 70 and 30% for calibration and testing stages, respectively. These sets considered five non-dimensional parameters, including (orifice crest height, orifice length, orifice height, upstream flow depth, and Froude number of the main channel). Results showed that the MLR and MNLR models in the calibrating stage had higher determination coefficients and lower errors. In addition, the importance of the input parameters was investigated, showing that the orifice crest height and Froude number highly affect the discharge coefficient value by 36%. In the testing stage, the estimated discharge coefficient by the MLR and MNLR models stayed within the range ±12 and ‡5%, respectively, of the experimental values. The MNLR model demonstrated a high level of equivalence compared to previous studies, which provided a mathematical expression to easily predict the TSO\u27s discharge coefficient