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Abstract:In order to predict the phreatic line, a method based on extreme learning machine (ELM) is proposed. The ELM network could well describe the nonlinear relationship between the seepage line and its influencing factors. The five main factors of seepage line included the minimum dry beach length, reservoir water level, seepage flow, vertical diplacement and horizontal displacement，which were used as the input of the ELM network, and the height of the phreatic line was used as the output of the network. In order to improve the prediction accuracy of ELM, the normalization method, activation function, and number of hidden layers are selected using the mean square error, and the maximum normalization method is finally determined to preprocess the data， which were entered the 5-9-1 ELM network and select the sin-type activation function. At the same time, the selected BP neural network that has the same normalization method and network form are used for comparison. The results show that ELM model has higher feasibility and better prediction accuracy in the short-term prediction of seepage line.
keywords: phreatic line prediction extreme learning machine tailings dam normalization mean square error
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|QIU Jun-bo||School of Civil Engineering,University of Science and Technology LiaoNingfirstname.lastname@example.org|
|HU Jun||School of Civil Engineering,University of Science and Technology LiaoNingemail@example.com|