A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of maximum annual flood discharges using artificial neural network approaches
The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.
Prediction of maximum annual flood discharges using artificial neural network approaches
The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.
Prediction of maximum annual flood discharges using artificial neural network approaches
Tugce Anilan (author) / Sinan Nacar (author) / Murat Kankal (author) / Omer Yuksek (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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