Simulation of Pore Water Pressure in the Body of Earth Dams During Construction by Combining Artificial Neural Network and Meta-Heuristic Algorithms

Authors

1 PhD Student, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

2 Professore, Department of Water Engineering, University of Tabriz, Iran

3 Associate Professor, University of Tabriz /Department of Water Engineering

4 Assistant Professor, University of Tabriz /Department of Water Engineering

Abstract

Accurate prediction of pore water pressure in the body of earth dams during construction is one of the most important factors in managing the stability of earth dams. In this study, using three different evolutionary neural network models including multilayer perceptron neural network with genetic algorithm, particle swarm optimization and Imperialist Competitive algorithm for estimating the pore water pressure in the body of Kabudwal earth dam at the time of construction, has been studied. Five features including fill level, construction time, reservoir level, impounding rate and fill speed during the 4-year statistical period were selected as the input of the hybrid model in piezometer EP19.7. The composition of the inputs was obtained using the feature selection method and the hybrid water cycle algorithm -artificial neural network. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level, construction time, reservoir level and dewatering speed were selected as the top four inputs, because the combination of these four features with MSE value of 1.1587 had the least error. In this study, artificial neural network weights are optimized to increase efficiency using the above three meta-heuristic algorithms. In general, according to statistical indicators, the results indicate acceptable accuracy of all three hybrid models. In terms of priority, the ANN-GA hybrid model with the highest accuracy and minimum error and values of , RMSE and MAE are equal to 0.9773, 0.0457 and 0.0399, respectively, is first priority and ANN-PSO and ANN-ICA hybrid models were given the next priorities, respectively.

Keywords

Main Subjects

References

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Water and Soil Science
Volume 33, Issue 1
March 2023
Pages 63-77

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