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whale optimization » swarm optimization (Expand Search)
work optimization » wolf optimization (Expand Search), swarm optimization (Expand Search), dose optimization (Expand Search)
binary plddt » linear plddt (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a whale » a whole (Expand Search), _ whale (Expand Search), a while (Expand Search)
whale optimization » swarm optimization (Expand Search)
work optimization » wolf optimization (Expand Search), swarm optimization (Expand Search), dose optimization (Expand Search)
binary plddt » linear plddt (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a whale » a whole (Expand Search), _ whale (Expand Search), a while (Expand Search)
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1
Hyperparameters of the LSTM Model.
Published 2025“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”
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2
The AD-PSO-Guided WOA LSTM framework.
Published 2025“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”
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3
Prediction results of individual models.
Published 2025“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”