Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Rainfall modeling using two different neural networks improved by metaheuristic algorithms

Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including�multilayer perceptron (MLP)�Henry gas solubility optimization (HGSO), MLP�bat algorithm (MLP�BA), MLP�particle swarm optimization (MLP�PSO), radial basis neural network function (RB...

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Bibliographic Details
Main Authors: Sammen S.S., Kisi O., Ehteram M., El-Shafie A., Al-Ansari N., Ghorbani M.A., Bhat S.A., Ahmed A.N., Shahid S.
Other Authors: 57192093108
Format: Article
Published: Springer 2024
Subjects:
Markov chain
MLP
Probability matrix
Rainfall modelling
RBFNN
Malaysia
Forecasting
Markov processes
Multilayer neural networks
Particle swarm optimization (PSO)
Soft computing
Water management
Gas solubility
Mean absolute error
Multilayers perceptrons
Network functions
Optimisations
Probability matrixes
Radial base neural network function
Radial basis neural networks
Testing process
climate modeling
climate prediction
optimization
rainfall
Rain
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