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      Evaluation of Urban Tourism Carrying Capacity Based on Analytic Hierarchy Process Optimizing BP Neural Network

      research-article
      1 , 2 ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          With the rapid development of today's social economy, tourism has also developed rapidly. According to national statistics, from 2017 to 2019, domestic tourism revenue increased from 4.57 trillion to 5.73 trillion. The tourism economy has made more and more contributions to the national economy, and it has also received more and more attention and attention from society. However, in recent years, the “explosive” growth of tourism has not only promoted economic development but also brought some challenges to society and the economy, such as environmental pollution in tourist cities. Therefore, it is of great significance to evaluate the tourism carrying capacity of a tourist destination city to realize the sustainable development of the city's tourism. This article aims to study the evaluation of urban tourism carrying capacity based on AHP and an optimized BP neural network. It designs a carrying capacity evaluation system, conducts BP neural network training for the system, and conducts system testing. The results show that the proportion of scientific and technological innovation is obviously higher than that of other aspects in the proportion of carrying capacity indicators in various aspects of each city. Environmental carrying capacity indicators can be divided into resource supply indicators, pollutant containment indicators, and social impact indicators. This article divides the important indicators into economic development, technological innovation, potential competition, environmental support, and development guarantee. Its indicators account for about 50%, with an average of more than 40%. This shows that the system can clearly display the main factors and evaluation indicators that affect the urban tourism carrying capacity and has certain feasibility and reliability.

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          Most cited references21

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          The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City

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            Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm

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              Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                16 May 2022
                : 2022
                : 5991381
                Affiliations
                1College of History, Culture and Tourism, Sichuan Normal University, Chengdu 610066, Sichuan, China
                2College of International Education, Sichuan Normal University, Chengdu 610066, Sichuan, China
                Author notes

                Academic Editor: Kapil Sharma

                Author information
                https://orcid.org/0000-0003-2384-0865
                Article
                10.1155/2022/5991381
                9126671
                35615556
                9162f518-febd-42d4-a387-6675c9b5d0f5
                Copyright © 2022 Jia Li and Yuan Wang.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 March 2022
                : 12 April 2022
                : 23 April 2022
                Funding
                Funded by: National Social Science Foundation Research on the Evolution, Promotion and Sustainable Development of Tourism Competitiveness
                Award ID: 20XJY017
                Categories
                Research Article

                Neurosciences
                Neurosciences

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