ArticleEnvironmental science and pollution research international2021
Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.Scientific reports · 2024Article
- Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH.Environmental science and pollution research international · 2022Article
- Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.Environmental science and pollution research international · 2021Article
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4 authors.
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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 R
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