Tourism Management Studies

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Publication Ethics

Memorandum of Understanding

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Article Writing Guide

Fails

Consultants

Consultants

Consultants

Consultants

Consultants

Consultants

Consultants

Publons

This letter of scientific publications

Methodology for evaluation of scientific publications

آیین نامه اجرایی قانون پیگیری و مقابله با تقلب در تهیه آثار علمی

Identification of Potential Tourism Centers Based on Environmental Factors in the West of Isfahan Province Using the Random Forest Algorithm

Document Type : Research Paper

Authors

دانشگاه اصفهان، خیابان هزارجریب

Abstract

This research leverages machine learning techniques to predict promising tourist destinations in the western region of Isfahan Province. A random forest prediction model is employed to identify the factors influencing tourist attraction in these areas and subsequently predict potential tourist hotspots. The input data encompasses 34 sites, including springs, waterfalls, shrines, museums, historical structures, and more. Variables such as elevation, slope, aspect, vegetation cover, distance to rivers, distance to roads, temperature, and precipitation were selected as the most significant predictive variables based on the Variance Inflation Factor (VIF). Evaluation metrics reveal that the random forest model exhibits superior predictive performance, achieving the highest R² (0.91) and the lowest RMSE (1.06) and MAE (1.13). Slope, slope aspect, vegetation cover, and distance to rivers and roads were identified as the most critical predictors. Moreover, the spatial prediction map indicates that Chadgan and daran possess high potential for tourist reception.

Keywords

Main Subjects

References
  1. Akin, M. (2015). A novel approach to model selection in tourism demand modeling. Tourism Management, 48, 64–72. http://dx.doi.org/10.1016/j.tourman.2014.11.004.
  2. Abang, Z., Abang, A., Wan F., Wan Y. , Syerina, A., Serah J., & Suhaili M., (2022). Using machine learning to predict visitors to totally protected areas in Sarawak, Malaysia. Sustainability, 14, 2735.https://doi.org/10.3390/su14052735.
  3. Bakhshandeh, E. , Zeraatpisheh, M. , Soleimanid, A. & Francaviglia, R. (2022). Land use conversion, climate change and soil organic carbon: Modeling a citrus garden chronosequence in Northern Iran. Geoderma Regional, e00559. https://doi.org/10.1016/j. geodrs. 2022. e00559.
  4. Chen, K. Y., Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28, 215–226. https://doi.org/10.1016/j.tourman.2005.12.018.
  5. Catani, F. , Lagomarsino, D. , Segoni, S. , Tofani, V. (2013). Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Natural Hazards and Earth System Sciences. 13, 2815-2831. http://dx.doi.org/10.5194/nhess-13-2815-2013.
  6. Claveria, O., Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220–228. https://doi.org/10.1016/j.econmod.2013.09.024.
  7. Castro, E. V., Souza, T. B., Thapa, B. (2015). Determinants of tourism attractiveness in the national parks of Brazil. Parks J. 21, 51–62. 10.2305/IUCN.CH.2014.PARKS-21-2EVDC.en.
  8. Claveria, O Monte, E., & Torra, S. (2015). Combination forecasts of tourism demand with machine learning models. AQR-IREA (Institute of Applied Economics Research), University of Barcelona. http://www.tandfonline.com/doi/abs/10.1080/13504851.2015.1078441?journalCode=rael20.
  9. Chen, W. , Shahabi, H. , Zhang, S. , Khosravi, K. ,Shirzadi, A. , Chapi, K. , …& Ahmad, B. (2018). Landslide susceptibility modeling based on Gis and novel bagging- based kernel logistic regression. Applied Science, 8(12), 254. https://doi.org/10.3390/app8122540.
  10. Dadashpour Moghaddam, M., Ahmadzadeh, H., & Valizadeh, R. (2022). A GIS-based assessment of urban tourism potential with a branding approach utilizing hybrid modeling. Spat. Information Research, 30(3), 399–416. https://doi.org/10.1007/s41324-022-00439-4.
  11. Golroo, A. H., Fani, H., & Naseri, M. (2021). Travel time modelling of urban roads by application of coyote optimization-based machine learning method. Amirkabir Journal of Civil Engineering. 53(9), 809-812. DOI: 10.22060/ceej.2020.17991.6730
  12. Hall, C., & Stephan, J. P. (1999). The geography of tourism and recreation: Environment, place and space. Rutledge. https://perpus.univpancasila.ac.id/repository/EBUPT190952.pdf.
  13. Hong, W., Dong, Y., Chen, L., & Wei, S. (2011). SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing 11, 1881–1890. https://doi.org/10.1016/j.asoc.2010.06.003.
  14. Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., Sila, , MacMillan, R. A., DeJesus, J. M., & Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLOS ONE, 10(6), e0125814. https://doi.org/10.1371/journal.pone.0125814.
  15. Höpken,W., Eberle, T., Fuchs, M., Lexhagen, M.(2020) Improving tourist arrival prediction: A big data and artificial neural network approach. Journal Travel Research, 60(8), 998–1017. https://doi.org/10.1177/0047287520921244.
  16. Lin, C., Chen, H., & Lee, T. (2011). Forecasting tourism demand using time series, artificial neural networks and multivariate adaptive regression splines: Evidence from Taiwan. International Journal of Business Administration, 2, 14–24. http://dx.doi.org/10.5430/ijba.v2n2p14.
  17. Li, Q., Huang, D., Pei, S., Qiao, J., & Wang, M. (2021). Using physical model experiments for hazards assessment of rainfall-induced debris landslides. Journal Earth Science, 32, 1113–1128. https://doi.org/10.1007/s12583-020-1398-3.
  18. Liu, A., Kim, Y. R. & Song, H. (2022). Toward an accurate assessment of tourism economic impact: A systematic literature review, Journal of Annals of Tourism Research Empirical Insights, 3(2), 100054. https://doi.org/10.1016/j.annale.2022.100054.
  19. Mobaraki, O. (2023). Spatial classification of tourism routes in Isfahan Province, Iran. Human Geographies – Journal of Studies and Research in Human Geography. 17(2). http://dx.doi.org/10.5719/hgeo.2023.172.3.
  20. Neuvonen, M., Pouta, E., Puustinen, J., & Sievanen, T. (2019). Visits to national parks: Effects of park characteristics and spatial demand. Journal for Nature Conservation, 18(4), 224–229.
  21. Nachappa, T. G., Ghorbanzadeh, O., Gholamnia, K., & Blaschke, T. (2020). Multi-hazard exposure mapping using machine learning for the state of salzburg, Austria. Remote Sensing, 12(17), 2757. doi: 10.3390/rs12172757.
  22. Naimi, S., Ayoubi, S., Zeraatpisheh, M., & Dematte, J. A. M. (2021). Ground observations and environmental covariates integration for mapping of soil salinity: A machine learning-based approach. Remote Sensing 13, 4825. doi:10.3390/rs13234825.
  23. Naimat, U. K., Wanggen, W., Rabia, R., Shuitao, J., & Xuzhi, W. (2023). Prediction and classification of user activities using machine learning models from location-based social network data. Applied Science. 13(6), 3517. https://doi.org/10.3390/app13063517.
  24. Padhi, S. S., Aggarwal, V. (2011). Competitive revenue management for fixing quota and price of hotel commodities under uncertainty. International Journal of Hospitality Management, 30,725–734. http://dx.doi.org/10.1016/j.ijhm.2010.12.007.
  25. Petrović, D., Ćebić, B., & Beljić, D. (2020). Predicting the number of tourist  using machin lerning, Turističko poslovanje, 28, 41. DOI 10.5937/turpos0-31845.
  26. Sun, S., Wei, Y., Tsui, K. L. & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1-10. https://doi.org/10.1016/j.tourman.2018.07.010.
  27. UNWTO (2015). Tourism highlights, World Tourism Organization, Madrid. doi/book/10.18111/9789284416899.
  28. Vu, H. D., Nguyen, N. T. P., Ngo, Y. T. H. & Le, T. D. (2022). Geotorism current state and future prospects: a case study in the Cao bang UNESCO global Geopark, Vietnam, GeoJournal of Tourism and Geosites, 43, (3), 1063–1070. . https://doi.org/10.30892/gtg.43327-921.
  29. Xu, A., Wang, C., Tang, D., & Ye, W. (2022), Tourism circular economy: Identification and measurement of tourism industry ecologization, Journal of Ecological Indicators, 144, 23-31. https://doi.org/10.1016/j.ecolind.2022.109476  
  30. Wu, Q., Law, R., Xu, X. (2012). A spare Gaussian process regression model for tourism demand forecasting in Hong Kong. Expert Systems with Applications, 39, 4769–4774. https://doi.org/10.1016/j.eswa.2011.09.159.
  31. Zeraatpisheh, M. , Garosi, Y. , Owliaie, H. R. , Ayoubi, S. , Taghizadeh-Mehrjardi, R., Scholten, T., & Xu, M. (2022). Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates. Catena 208, 105723. http://dx.doi.org/10.1016/j.catena.2021.105723.
  32. Baqideh, M., Alijani, B., & Ziaian, P. (2011). Investigating the possibility of using the NDVI vegetation index in analyzing droughts in Isfahan province. Journal of Geographic Studies of Arid Regions, 1(4), 1-16. https://sid.ir/paper/190688/fa. [In Persian]
  33. Fallah Tafti, H. (2021). Forecasting the number of tourists based on Google Trends data using machine learning methods (Case study: Tourists in Yazd city). Scientific-Research Journal of Tourism and Development, 10(2), 67-79. doi: 10.22034/jtd.2020.217294.1952. [In Persian]
  34. Ghaffari, R. (2009). Prioritization of investment and location of tourism facilities in tourist hubs of Chaharmahal and Bakhtiari Province. Cultural Heritage, handicrafts and Tourism Organization of Chaharmahal and Bakhtiari Province. [In Persian]
  35. Hatefi, M., Kouhi Habibi, N., & Abdollahi, A. (2019). Evaluation of tourism hubs suitable for investment using the integrated fuzzy Shannon entropy model and fuzzy ARAS method. Journal of Tourism Management Studies, 14(48), 269-302. https://doi.org/10.22054/tms.2020.30579.1887[In Persian]
  36. Ranjbarian, B., Khazaei Pool, J., & Balouei Jamkhaneh, h. (2012).  An analysis of strengths, weaknesses, opportunitties, and threats of outbound tourism industry In Isfahan by Fuzzy AHP. Journal of Ournal of Tourism Planning and  Developmente, 1(1) , 13-34. https://www.sid.ir/paper/245972/fa . [In Persian]
  37. Safa, G. (2013). Assessment of the ecotourism potential of isfahan using a multi-criteria evaluation method, [Master's Thesis, Isfahan University of Technology]. https://library.iut.ac.ir/dL/search/default.aspx?Term=8923&Field=0&DTC=107. [In Persian]
  38. Shah Zidi, M., Mohammadi, S., & Bagheri, N. (2020). Identifying the most important tourism issues in the city of Isfahan. Journal of Social Tourism Studies, 8(15), 193-220. https://sid.ir/paper/362214/fa.
  39. Talebi, A., Goudarzi, S., & Pour Qasemi, H. R. (2018). Investigating the possibility of preparing a landslide hazard map using the random forest algorithm (Study area: Sardar Abad watershed, Lorestan Province). Journal of Natural Hazards, 7(16), 45-64. doi: 22111/JNEH.2017.3213. [In Persian]
  40. Yousefian Darani, R., Khosrowzadeh, A., Heydarian, M. (2017). Cultural interaction in the copper age of the Zayandeh Rud watershed in Chadegan, Fereydun, and Fereydunshahr with Neighboring Regions based on the study and typology of pottery obtained from archaeological surveys. (20). [In Persian]
Tourism Management Studies
Volume 20, Issue 70
July 2025
Pages 327-358
Files
Share
How to cite
Statistics