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

• 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
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-021-15029-4

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.