Prediction of dike seepage pressure based on ISSA-BiLSTM
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Abstract
The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum. The sparrow search algorithm (SSA) was improved as ISSA using both methods of nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio. We combined the Bidirectional Long Short-Term Memory (BiLSTM) neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model. The critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction. The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations, achieving an R2 of 0.987. In comparison to SSA-BiLSTM and BiLSTM, the model exhibits a substantial reduction in errors, decreasing by approximately 20% and 30%, respectively. This model can provide technical support and insights for accurately predicting dam seepage, contributing to the advancement of this field.
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