Abstract
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 R2= 0.957 and RMSE= 0.806 and in the testing phase were equal to R2= 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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Azamathulla HM, Haghiabi AH, Parsaie A (2016) Prediction of side weir discharge coefficient by support vector machine technique. Water Sci Technol Water Supply 16:1002–1016. https://doi.org/10.2166/ws.2016.014
Brameier M, Banzhaf W (2007) Linear genetic programming
Dashti Latif S, Najah Ahmed A, Sherif M, Sefelnasr A, el-Shafie A (2020) Reservoir water balance simulation model utilizing machine learning algorithm. Alexandria Eng J 60:1365–1378. https://doi.org/10.1016/j.aej.2020.10.057
Ehteram M, Ahmed AN, Latif SD, Huang YF, Alizamir M, Kisi O, Mert C, el-Shafie A (2020a) Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environ Sci Pollut Res 28:1596–1611. https://doi.org/10.1007/s11356-020-10421-y
Ehteram M, Ahmed AN, Ling L, Fai CM, Latif SD, Afan HA, Banadkooki FB, el-Shafie A (2020b) Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm. Water (Switzerland) 12:902. https://doi.org/10.3390/w12030902
Ehteram M, Yenn F, Najah A et al (2020c) Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System ( ANFIS ) with optimization algorithms. Ain Shams Eng J 11:1665–1676. https://doi.org/10.1016/j.asej.2020.08.019
Etemad-Shahidi A, Taghipour M (2012) Predicting longitudinal dispersion coefficient in natural streams using M5′ model tree. J Hydraul Eng 138:542–554. https://doi.org/10.1061/(asce)hy.1943-7900.0000550
Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 1:364–378. https://doi.org/10.1109/TSMC.1971.4308320
Jumin E, Basaruddin FB, Yusoff YBM, Latif SD, Ahmed AN (2021) Solar radiation prediction using boosted decision tree regression model: a case study in Malaysia. Environ Sci Pollut Res 28:26571–26583. https://doi.org/10.1007/s11356-021-12435-6
Lai V, Malek MA, Abdullah S et al (2020) Time-series prediction of sea level change in the east coast of Peninsular Malaysia from the supervised learning approach. Int J Des Nat Ecodynam 15:409–415. https://doi.org/10.18280/ijdne.150314
Latif SD (2021) Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-12877-y
Latif SD, Ahmed AN (2021) Application of deep learning method for daily streamflow time-series prediction: a case study of the Kowmung River at Cedar Ford, Australia. International Journal of Sustainable Development and Planning. https://doi.org/10.18280/ijsdp.160310
Latif SD, Azmi MSBN, Ahmed AN et al (2020) Application of artificial neural network for forecasting nitrate concentration as a water quality parameter: a case study of Feitsui Reservoir, Taiwan. Int J Des Nat Ecodynam 15:647–652. https://doi.org/10.18280/ijdne.150505
Latif SD, Ahmed AN, Sathiamurthy E, Huang YF, El-Shafie A (2021a) Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia. Natural Hazards. https://doi.org/10.1007/s11069-021-04839-x
Latif SD, Marhain S, Hossain S et al (2021b) Optimizing the operation release policy using charged system search algorithm: a case study of Klang Gates Dam , Malaysia. Sustain 13:19. https://doi.org/10.3390/su13115900
Markovic M, Brankovic JM, Stosovic MA, Zivkovic S, Brankovic B (2021) A new method for pore pressure prediction on malfunctioning cells using artificial neural networks. Water Resour Manag 35:979–992. https://doi.org/10.1007/s11269-021-02763-0
Najafzadeh M (2016) Neurofuzzy-based GMDH-PSO to predict maximum scour depth at equilibrium at culvert outlets. J Pipeline Syst Eng Pract 7:06015001. https://doi.org/10.1061/(asce)ps.1949-1204.0000204
Najafzadeh M, Bonakdari H (2017) Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers. J Pipeline Syst Eng Pract 8:06016003. https://doi.org/10.1061/(asce)ps.1949-1204.0000249
Najafzadeh M, Kargar AR (2019) Gene-expression programming, evolutionary polynomial regression, and model tree to evaluate local scour depth at culvert outlets. J Pipeline Syst Eng Pract 10:04019013. https://doi.org/10.1061/(asce)ps.1949-1204.0000376
Najafzadeh M, Tafarojnoruz A (2016) Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers. Environ Earth Sci 75:1–12. https://doi.org/10.1007/s12665-015-4877-6
Najafzadeh M, Saberi-Movahed F, Sarkamaryan S (2018) NF-GMDH-based self-organized systems to predict bridge pier scour depth under debris flow effects. Mar Georesour Geotechnol 36:589–602. https://doi.org/10.1080/1064119X.2017.1355944
Najah A, Teo FY, Chow MF, Huang YF, Latif SD, Abdullah S, Ismail M, el-Shafie A (2021) Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: case studies in Malaysia. Int J Environ Sci Technol 18:1009–1018. https://doi.org/10.1007/s13762-021-03139-y
Nourani V, Babakhani A (2013) Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modeling. J Comput Civ Eng 27:183–195. https://doi.org/10.1061/(asce)cp.1943-5487.0000200
Parsaie A (2016) Predictive modeling the side weir discharge coefficient using neural network. Model Earth Syst Environ 2:63. https://doi.org/10.1007/s40808-016-0123-9
Quinlan JR (1992) Learning with continuous classes. Aust Jt Conf Artif Intell 92:343–348
Ranković V, Novaković A, Grujović N, Divac D, Milivojević N (2014) Predicting piezometric water level in dams via artificial neural networks. Neural Comput Applic 24:1115–1121. https://doi.org/10.1007/s00521-012-1334-2
Rehamnia I, Benlaoukli B, Jamei M, Karbasi M, Malik A (2021) Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: case study of Fontaine Gazelles Dam, Algeria. Meas J Int Meas Confed 176:109219. https://doi.org/10.1016/j.measurement.2021.109219
Sharghi E, Nourani V, Behfar N (2018) Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. J Hydroinf 20:1071–1084. https://doi.org/10.2166/hydro.2018.151
Tayfur G, Swiatek D, Wita A, Singh VP (2005) Case study: finite element method and artificial neural network models for flow through Jeziorsko Earthfill Dam in Poland. J Hydraul Eng 131:431–440. https://doi.org/10.1061/(asce)0733-9429(2005)131:6(431)
Wang Y, Witten IH (1996) Induction of model trees for predicting continuous classes. The University of Waikatod: University of Waikato, Department of Computer Science. Permanent Research Commons link: https://hdl.handle.net/10289/1183
Availability of data and materials
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. All data, models, and code generated or used during the study appear in the submitted article.
Author information
Authors and Affiliations
Contributions
Amir Hamzeh Haghiabi: research group manager. Sarmad Dashti Latif: review and editing. Abbas Parsaie: modelling, running the soft computing codes, and performing the simulation. Ravi Prakash Tripathi: soft computing programming.
Corresponding author
Ethics declarations
Conflict of interest
The author declares no competing interests.
Ethical approval
Not applicable
Consent to participate
Not applicable
Consent to publish
Not applicable
Additional information
Responsible Editor: Marcus Schulz
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Parsaie, A., Haghiabi, A.H., Latif, S.D. et al. Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models. Environ Sci Pollut Res 28, 60842–60856 (2021). https://doi.org/10.1007/s11356-021-15029-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-15029-4