Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models

Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron neural network (MLPNN), support vector machine (SVM...

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Bibliographic Details
Published in:Environmental science and pollution research international 2021-11, Vol.28 (43), p.60842-60856
Main Authors: Parsaie, Abbas, Haghiabi, Amir Hamzeh, Latif, Sarmad Dashti, Tripathi, Ravi Prakash
Format: Article
Language:English
Subjects:
Algorithms
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Computer applications
Dam design
Dam engineering
Design parameters
Discharge
Earth and Environmental Science
Earth dams
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Genetic algorithms
Group method of data handling
Mars
Mathematical models
Multilayers
Neural networks
Piezometric head
Pore pressure
Prediction models
Research Article
Seepage
Soft computing
Support vector machines
Waste Water Technology
Water Management
Water Pollution Control
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Summary:Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron neural network (MLPNN), support vector machine (SVM), multivariate adaptive regression splines (MARS), genetic programming (GP), M5 algorithm, and group method of data handling (GMDH) were used to predict the piezometric head in the core and the seepage discharge through the body of earth dam. For this purpose, the data recorded by the absolute instrument during the last 94 months of Shahid Kazemi Bukan Dam were used. The results showed that all of the applied models had a permissible level of accuracy in the prediction of the piezometric heads. The average error indices for the models in the training phase were R 2 = 0.957 and RMSE= 0.806 and in the testing phase were equal to R 2 = 0.949 and RMSE= 0.932, respectively. The performances of all models except the M5 and MARS in predicting seepage discharge are nearly identical; however, the best is the MARS, and the weakest is the M5 algorithm.
ISSN:0944-1344
1614-7499
DOI:10.1007/s11356-021-15029-4