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Estimation of water's surface elevation in compound channels with converging and diverging floodplains using soft computing techniques
In this research, water's surface elevation in compound channels with converging and diverging floodplains using soft computing models including the Multi-Layer Perceptron Neural Network (MLPNN), Group Method of Data Handling (GMDH), Neuro-Fuzzy Group Method of Data Handling (NF-GMDH) and Support Vector Machine (SVM) was modeled and predicted. For this purpose, laboratory data published in this field were used. Parameters including convergence angle (with a positive sign) and divergence angle (with a negative sign), relative depth, and relative distance were used as input variables. The results showed that all the used models have appropriate performance. However, the best performance was related to the SVM model with statistical indicators R2 = 0.998 and RMSE = 0.008 in the testing stage. The use of the adaptive fuzzy approach in developing the GMDH model led to a remarkable increase in accuracy so that its statistical indicators in the testing stage reached R2 = 0.985 and RMSE = 0.203. It was found that the best performance of the activation and kernel functions in the development of the MLPNN model and the SVM is related to the sigmoid and radial tangent functions. HIGHLIGHT Water’s surface elevation in compound channels with converging and diverging floodplains using Multi-Layer Perceptron Neural Network (MLPNN).; Water’s surface elevation in compound channels with converging and diverging floodplains using Group Method of Data Handling (GMDH).; Water’s surface elevation in compound channels with converging and diverging floodplains using Neuro-Fuzzy Group Method of Data Handling (NF-GMDH).; Water’s surface elevation in compound channels with converging and diverging floodplains using Support Vector Machine (SVM).;
Estimation of water's surface elevation in compound channels with converging and diverging floodplains using soft computing techniques
In this research, water's surface elevation in compound channels with converging and diverging floodplains using soft computing models including the Multi-Layer Perceptron Neural Network (MLPNN), Group Method of Data Handling (GMDH), Neuro-Fuzzy Group Method of Data Handling (NF-GMDH) and Support Vector Machine (SVM) was modeled and predicted. For this purpose, laboratory data published in this field were used. Parameters including convergence angle (with a positive sign) and divergence angle (with a negative sign), relative depth, and relative distance were used as input variables. The results showed that all the used models have appropriate performance. However, the best performance was related to the SVM model with statistical indicators R2 = 0.998 and RMSE = 0.008 in the testing stage. The use of the adaptive fuzzy approach in developing the GMDH model led to a remarkable increase in accuracy so that its statistical indicators in the testing stage reached R2 = 0.985 and RMSE = 0.203. It was found that the best performance of the activation and kernel functions in the development of the MLPNN model and the SVM is related to the sigmoid and radial tangent functions. HIGHLIGHT Water’s surface elevation in compound channels with converging and diverging floodplains using Multi-Layer Perceptron Neural Network (MLPNN).; Water’s surface elevation in compound channels with converging and diverging floodplains using Group Method of Data Handling (GMDH).; Water’s surface elevation in compound channels with converging and diverging floodplains using Neuro-Fuzzy Group Method of Data Handling (NF-GMDH).; Water’s surface elevation in compound channels with converging and diverging floodplains using Support Vector Machine (SVM).;
Estimation of water's surface elevation in compound channels with converging and diverging floodplains using soft computing techniques
Sajad Bijanvand (author) / Mirali Mohammadi (author) / Abbas Parsaie (author)
2023
Article (Journal)
Electronic Resource
Unknown
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