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

• Main Authors: Gholami M., Ghanbari-Adivi E., Ehteram M., Singh V.P., Najah Ahmed A., Mosavi A., El-Shafie A.
• Other Authors: 56973673400
• Format: Article
• Published: Ain Shams University 2024
• Subjects: Artificial intelligence; Big data; Deep learning; Longitudinal dispersion coefficient; Machine learning; Multilayer perceptron; Optimization; Dispersions; Environmental management; Forecasting; Learning systems; Multilayer neural networks; Multilayers; Water resources; Firefly algorithms; Machine-learning; Multilayers perceptrons; Optimisations; Optimization algorithms; Particle swarm optimization algorithm; Salp swarms; Swarm algorithms; Particle swarm optimization (PSO)

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. � 2023 THE AUTHORS