Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models

Abstract

Prediction of the longitudinal dispersion coefficient (LDC) is essential for the river and water resources engineering and environmental management. This study proposes ensemble models for predicting LDC based on multilayer perceptron (MULP) methods and optimization algorithms. The honey badger optimization algorithm (HBOA), salp swarm algorithm (SASA), firefly algorithm (FIFA), and particle swarm optimization algorithm (PASOA) are used to adjust the MULP parameters. Then, the outputs of the MULP-HBOA, MULP-SASA, MULP-PASOA, MULP-FIFA, and MULP models were incorporated into an inclusive multiple model (IMM). For IMM at the testing level, the mean absolute error (MEAE) was 15, whereas it was 17, 18, 23, 24, and 25 for the MULP-HBOA, MULP-SASA, MULP-FIFA, MULP-PASOA, and MULP models. The study also modified the structure of MULP models using a goodness factor which decreased the CPU time. Removing redundant neurons reduces CPU time. Thus, the modified ANN model and the suggested IMM model can decrease the computational time and further improve the performance of models.

Keywords

• Longitudinal dispersion coefficient
• Multilayer perceptron
• Optimization
• Artificial intelligence
• Machine learning
• Deep learning