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

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

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 opti...

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主要な著者:	Gholami M., Ghanbari-Adivi E., Ehteram M., Singh V.P., Najah Ahmed A., Mosavi A., El-Shafie A.
その他の著者:	56973673400
フォーマット:	論文
出版事項:	Ain Shams University 2024
主題:	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)
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Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models

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