Elman-GA Security Capacity Management Strategy for Tourist Attractions

With the booming development of the tourism industry, scenic spots often face the risk of tourist overload during peak hours. In response to the weak dynamic adaptability of traditional safety management methods and the difficulty in accurately predicting passenger flow in high-density scenarios, a safety capacity management method based on Elman Recurrent Neural Network Genetic Algorithm (Elman-GA) is proposed. This method uses Elman neural network for passenger flow prediction, and decomposes the raw data for denoising through Complete Ensemble Empirical Mode Decomposition (CEEMD). Using an improved GA as the optimization engine and designing a simulated annealing mechanism, the network is deeply coupled with the algorithm to achieve intelligent and secure capacity management decisions. The experimental findings reveal that the prediction accuracy of the research method is tested, and the lowest Root Mean Square Error (RMSE) reaches 12.3% when the window length is 6 h. When testing the safety control effect of the research approach, the highest F1 score is 0.91 when the warning threshold reaches 85%. The robustness of the research approach is assessed, and when the noise amplitude increases to 30%, the RMSE reaches 35.4%. The prediction results of the research method are more accurate and stable, which can offer technical foundation to the establishment of smart tourism scenic spots.