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STUDY ON ECOTOURISM DEVELOPMENT BASED ON DEEP NETWORK ASSOCIATION

机译：基于深网络协会的生态旅游发展研究

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Accurate tourism prediction is of great significance to tourism policy-making authorities and tourists, which can help to allocate resources rationally and avoid congestion and stranded tourists. In order to improve the accuracy of tourism prediction, this thesis proposes a depth neural network prediction model based on network search considering the interference of noise in the prediction. In this model, the depth neural network method is used to synthesize the network search data exponentially, the ELM chaotic strategy is used to deal with the noise of the sequence, the high frequency noise is separated from the original sequence, and then the denoising network search data is used to predict the tourist flow. Make empirical analysis of Fuxi mountain as an example to predict the next 22 weeks of tourists. It is found that the prediction error of deep neural network based on network search is significantly lower than that of time series, network search and BP neural network. On the one hand, this conclusion shows the improvement of the prediction model, on the other hand, it also shows the necessity of noise processing in prediction.
机译：准确的旅游预测对旅游政策制定的当局和游客具有重要意义，这可以帮助合理地分配资源，避免拥堵和搁浅的游客。为了提高旅游预测的准确性，本文提出了一种基于网络搜索的深度神经网络预测模型，考虑预测中的噪声的干扰。在该模型中，深度神经网络方法用于指数呈指数地合成网络搜索数据，使用ELM混沌策略来处理序列的噪声，高频噪声与原始序列分离，然后是去噪网络搜索数据用于预测旅游流程。对福锡山进行实证分析，以预测未来22周的游客。结果发现，基于网络搜索的深神经网络的预测误差显着低于时间序列，网络搜索和BP神经网络的预测误差。一方面，该结论显示了预测模型的改进，另一方面，它还显示了预测中噪声处理的必要性。

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