Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network

Cheng, Xu; Zhao, Chao

doi:10.18280/ria.330403

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…In addition, it can be seen that the MAPE of the model is increased after excluding the weather(2.09%), holidays(2.64%) and tourism demand in the same period of previous years(18.9%). Similar to the existing research [7,8,10,13], the effectiveness of weather, holidays and tourism demand in previous has been confirmed in this paper.…”

Section: B Results Discussionsupporting

confidence: 87%

“…(5) Tourism demand: Is the output data of prediction model, is a continuous variable. In the existing research [6,12,15], the tourism demand on the forecast day (some studies also call it as tourist flow [8,13] or tourism volume [7]) refers to the actual number of tourists on the forecast day, in the academic practice of the above citation, actual number of tourists on the forecast day is usually obtained from the actual statistics of scenic spots. In this paper, the number of people taking the scenic transportation vehicle is selected as the actual tourist passenger volume, due to the management regulations of Huangshan scenic area, all the tourists to Huangshan need to take the scenic transport.…”

Section: A Selection Of Variablesmentioning

confidence: 99%

“…Bi [7] proposed a long-short-term memory network (LSTM) as daily tourism prediction method and verified the effectiveness of the method through practical applications. Cheng [8] used the Deep Belief Neural Network (DBN) to predict the daily tourist passenger volume and compares it with the feedforward neural network and other models. The results show that DBM is significantly better than the other.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Forecast Model of Tourism Demand Driven by Social Network Data

et al. 2021

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To improve the forecasting accuracy of tourism demand through forecasting model and data sources, this paper takes the social network data as an entry point, and collects the social network data by the web crawler, then quantifies the data based on the sentiment analysis of the BERT model. This paper uses structured variables such as social network data, weather, holidays, etc. to build a tourism demand forecasting model based on Gradient Boosting Regression Trees. At last, take Huang Shan as example, use actual statistics of passenger terminal and social network data to do an empirical analysis of Huang Shan tourism demand forecasting. Compared with the existing model and introduce ablation study to verify the effectiveness of the considered factors. The result shows that the model based on social network data has improved the forecasting accuracy from the existing ones, ablation study shows social network data helps to improve the accuracy of tourism demand forecasting.

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Section: B Results Discussionsupporting

confidence: 87%

Section: A Selection Of Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Forecast Model of Tourism Demand Driven by Social Network Data

et al. 2021

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“…Since the emergence of BP neural networks, many scholars at home and abroad have studied and applied BP neural networks and their improvements [13]. Cheng and Zhao improved the activation function of RNN, which effectively accelerated the convergence speed of training [14]. Kang et al simplified the highway BP neural network based on which the relevant parameters were significantly reduced and the computational complexity was reduced [15].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network

Zhu

Liu

2022

Journal of Mathematics

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This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.

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“…Based on the above outstanding problems, this paper proposed an adaptive detection algorithm for micro-grid harmonic power based on DBN in Abdellaoui and Douik [17]. Specifically, DBN was introduced in the white noise signal processing process of EEMD in Cheng and Zhao [18]. The first object of the present study is to achieve adaptive matching with white noise signals according to the current signal data characteristics in Sbargoud et al [19], and the second one is to reduce the artificial setting error, so that the separation result can be closer to the theoretical value.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Detection Algorithm for Micro-grid Harmonic Power Based on Deep Belief Network

2021

Teh. vjesn.

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There are many non-linear load devices in the micro-grid, resulting in a lot of complex harmonics, which is a key problem that leads to low measurement accuracy of electric energy metering devices. The traditional integrated empirical mode decomposition (EEMD) method can effectively deal with the problem of nonlinear and nonstationary signals, but this method has the problem of being highly dependent on artificial pre-set parameters. Here, the deep belief network (DBN) is introduced in the white noise signal generation process of EEMD. The main problems solved are as follows: one is to adaptively match the white noise signal according to the data characteristics of the current signal in the micro-grid, the other is to reduce the artificial setting error and make the separation result closer to the theoretical value. Finally, this paper uses the operating data in the actual environment to carry out experimental verification, and the results show that the error between the value of harmonic power in the production environment and the theoretical value given is reduced by 9.73%.

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Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network

Cited by 8 publications

References 22 publications

A Forecast Model of Tourism Demand Driven by Social Network Data

A Forecast Model of Tourism Demand Driven by Social Network Data

Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network

An Adaptive Detection Algorithm for Micro-grid Harmonic Power Based on Deep Belief Network

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