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

•Accurate estimation of seepage flow through embankment dam using Extended Kalman Filter based neural network approach.•Validation of the EKF-ANN paradigm using the MLP, RBF-NN, and RF approaches.•Assessment of four efficient scenarios based on 10 input variables for predicting the seepage flow.•Outlier analysis to specify the applicability domains of the provided models. Seepage flow through embankment dam is one of the most influential factors in failures of them. Thus, the monitoring and accurate measuring of seepage are crucial for the safety and construction cost of an embankment dam. In this study, an efficient data-intelligence paradigm comprised of Extended Kalman Filter integrated with the Feed Forward type Artificial Neural Network (EKF-ANN) scheme, as the main novelty, was developed for precise estimation of the daily seepage flow through embankment dam in Fontaine Gazelles Dam in Algeria. Here, three robust machine learning approaches, namely the Multilayer Perceptron (MLP) Neural Networks, Radial Basis Function-Neural Networks (RBF-NN), and Random Forest (RF), were examined for evaluating the capability of the EKF-ANN in the prediction of seepage flow. According to the obtained results, the EKF-ANN paradigm outperformed the MLP, RF, and RBF-NN, respectively. Besides, the leverage approach was applied to report the applicability domain of provided models.