Rainfall modeling using two different neural networks improved by metaheuristic algorithms

• 主要な著者: Sammen S.S., Kisi O., Ehteram M., El-Shafie A., Al-Ansari N., Ghorbani M.A., Bhat S.A., Ahmed A.N., Shahid S.
• その他の著者: 57192093108
• フォーマット: 論文
• 出版事項: Springer 2024
• 主題: 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

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...