Combined CNN-BiLSTM-Att tourism flow prediction based on VMD-MWPE decomposition reconstruction.

Predicting tourist flow is crucial for making effective management decisions at scenic spots. To further improve the accuracy of scenic passenger flow prediction, the current study proposed a data preprocessing approach that combines variational modal decomposition (VMD) with multiscale weighted permutation entropy (MWPE). Additionally, a tourist flow prediction method was introduced, utilizing a combination of bi-layer convolution, bidirectional long short-term memory, and an attention mechanism (CNN-BiLSTM-Att). First, the historical passenger flow data was decomposed into multiple intrinsic modal functions using VMD. Second, the IMF subsequence was measured using the MWPE metric, leading to the construction of a new feature matrix. Finally, the CNN-BiLSTM prediction model, incorporating an attention mechanism and optimized using a genetic algorithm, was applied to generate the final prediction results. Using Lushan Scenic Area (China) as a case study, our experimental results demonstrate that the proposed model significantly outperforms benchmark approaches including XGBoost, LSTM and CNN-BiLSTM. The comparative analysis reveals consistent improvements across all evaluation metrics: achieving a minimum 7.46% reduction in the MAPE, at least 7.2% decrease in BRMSE, and a minimum 1.74% enhancement in the R