Spatiotemporal dynamics of geotourism resilience: Machine learning methodologies and key insights^☆

Abstract

The tourism industry, along with its various sub-sectors, offers numerous economic, social, and cultural benefits to host communities, particularly in rural areas. However, few studies have explored the prediction of geotourism resilience at the Likert scale using machine learning techniques, especially those focusing on feature selection through the 10-fold cross-validation technique. This study aims to predict geotourism resilience using several machine learning algorithms—including artificial neural networks (ANN), Bayes net (BN), logistic regression (LR), naive Bayes (NB), naive Bayes tree (NBTree), and random forest (RF)—in Quri-Qaleh village, Ravansar county, Kermanshah province, Iran. Data were collected from a randomly selected sample of 150 individuals through a structured questionnaire. The most important variables on geotourism resilience were selected based on the average merit of the information gain ratio (AM_IGR) feature selection technique. To evaluate the models' goodness-of-fit and predictive performance, several statistical metrics—including precision, recall, F1-measure, area under the curve (AUC)—as well as the non-parametric Friedman test, were applied. The findings revealed 65.3% of respondents exhibited low to very low resilience, 10.7% moderate resilience, and 24% high to very high resilience. Among the predictive variables, product innovation (PI; AM_IGR = 0.254), government support (GS; AM_IGR = 0.253), increased destination attractiveness (IDA; AM_IGR = 0.213), monthly income (MI; AM_IGR = 0.203) and social capital (SC; AM_IGR = 0.189) emerged as the five most influential variables. Comparative analysis indicated the NB algorithm outperformed other models in predicting geotourism resilience (mean of AUC = 0.826; F1-measure = 0.627; recall = 0.622, and precision = 0.651). This research can assist decision-makers in environmental and rural development in identifying strategies to strengthen the resilience of geotourism.

Keywords

Geotourism resilience / Likert scale / Machine learning algorithms / Adaptation and recovery / Product innovation

Author summay

^☆This article is part of a Special issue entitled: ‘GeoTrends’ published in International Journal of Geoheritage and Parks.

CRediT authorship contribution statement

Himan Shahabi: Writing - original draft, Supervision, Project administration, Investigation, Formal analysis, Conceptualization. Davood Jamini: Writing - original draft, Funding acquisition, Data curation. Ataollah Shirzadi: Writing - original draft, Validation, Software, Methodology, Investigation, Formal analysis. Hojatollah Sadeghi: Writing - original draft. Hossein Komasi: Writing - original draft, Resources. Ismail Elkhrachy: Writing - review & editing, Writing - original draft, Funding acquisition. Aryan Salvati: Writing - original draft, Validation, Methodology. John J. Clague: Writing - review & editing, Writing - original draft. Zahed Ghaderi: Writing - review & editing, Writing - original draft.

Ethical statement

Ethical approval was waived by the institutional ethics committee of University of Kurdistan because this study did not involve any sensitive data. The interviews were conducted on a voluntary basis, and the confidentiality and rights of the respondents were fully maintained throughout the study. Informed consent was obtained from all the respondents prior to the commencement of the research.

Funding

This research was funded by the University of Kurdistan, Iran (Grant No. 01-9-14939).

Declaration of competing interest

The authors declare the following financial interests (e.g., any funding for the research project)/personal relationships (e.g., the author is an employee of a profitable company) which may be considered as potential competing interests: Davood Jamini reports financial support was provided by the University of Kurdistan. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Acharya A., Mondal B. K., Bhadra T., Abdelrahman K., Mishra P. K., Tiwari A., & Das R. (2022). Geospatial analysis of geo-ecotourism site suitability using AHP and GIS for sustainable and resilient tourism planning in West Bengal, India. Sustainability, 14, 2422. https://doi.org/10.3390/su14042422.

[2] Assaf A., & Scuderi R. (2020). COVID-19 and the recovery of the tourism industry. London, England: SAGE Publications Sage UK, 731-733.

[3] Beirman D. (2018). Thailand’s approach to destination resilience: An historical perspective of tourism resilience from 2002 to 2018. Tourism Review International, 22, 277-292. https://doi.org/10.3727/154427218x15369305779083.

[4] Benítez-Aurioles B. (2020). Tourism resilience patterns in Southern Europe. Tourism Analysis, 25, 409-424. https://doi.org/10.3727/108354220x16010020096118.

[5] Bhamre N., Ekhande P. P., & Pinsky E. (2025). Enhancing naive Bayes algorithm with stable distributions for classification. Computer Science & Information Technology (CS & IT), 14(2), 107-116. https://doi.org/10.5121/ijci.2025.140207.

[6] Božovit T., Blešit I., Nedeljkovit-Kneževit M., Đeri L., & Pivac T. (2021). Resilience of tourism employees to changes caused by COVID-19 pandemic. Journal of the a)Geographical Institute “Jovan Cvijic”, SASA, 71, 181-194. https://doi.org/10.2298/ijgi2102181b.

[7] Breiman L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324.

[8] Breiman L., Friedman J., Olshen R., & Stone C. (1984). Classification and regression trees. New York: Chapman and Hall. https://doi.org/10.1201/9781315139470.

[9] Brocx M., & Semeniuk V. (2019). The ‘8Gs’—A blueprint for geoheritage, geoconservation, geo-education and geotourism. Australian Journal of Earth Sciences, 66, 803-821. https://doi.org/10.1080/09669582.2020.1717503.

[10] Brodsky E., & Darkhovsky B. S. (2000). Non-parametric statistical diagnosis:Problems and methods. Dordrecht: Springer. https://doi.org/10.1007/978-94-015-9530-8.

[11] Bui H. T., Jones T. E., Weaver D. B., & Le A. (2020). The adaptive resilience of living cultural heritage in a tourism destination. Journal of Sustainable Tourism, 28, 1022-1040. https://doi.org/10.1080/09669582.2020.1717503.

[12] Bui T. T. B., & Ngo T. P. Q. (2022). Factors impacting on tourism resilience during the COVID-19 pandemic: An empirical study from Vietnam. The Journal of Asian Fi- nance, Economics and Business, 9, 213-218. https://doi.org/10.13106/jafeb.2022.vol9.no1.0213.

[13] Carrión-Mero P., Turner-Carrión M., Herrera-Franco G., Bravo-Murillo G., Aguilar-Aguilar M., Paz-Salas N., & Berrezueta E. (2022). Geotouristic route proposal for touristic development in a mining area—Case study. Resources, 11, 25. https://doi.org/10.3390/resources11030025.

[14] Chen F., Xu H., & Lew A. A. (2020). Livelihood resilience in tourism communities: The role of human agency. Journal of Sustainable Tourism, 28, 606-624. https://doi.org/10.1080/09669582.2019.1694029.

[15] Chen W., Xie X., Peng J., Wang J., Duan Z., & Hong H. (2017). GIS-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomatics, Natural Hazards and Risk, 8, 950-973. https://doi.org/10.1080/19475705.2017.1289250.

[16] Cheung K. S., & Li L. -H. (2019). Understanding visitor-resident relations in overtourism: Developing resilience for sustainable tourism. Journal of Sustainable Tourism, 27, 1197-1216.

[17] Chiarini V., Duckeck J., & De Waele J. (2022). A global perspective on sustainable show cave tourism. Geoheritage, 14, 82. https://doi.org/10.1007/s12371-022-00717-5.

[18] De Freitas C. R. (2010). The role and importance of cave microclimate in the sustainable use and management of show caves. Acta Carsologica, 39(3), 477-489. https://doi.org/10.3986/ac.v39i3.77.

[19] Deng N., Li Y., Ma J., Shahabi H., Hashim M., de Oliveira G., & Chaeikar S. S. (2022). A comparative study for landslide susceptibility assessment using machine learn- ing algorithms based on grid unit and slope unit. Frontiers in Environmental Science, 10, 1009433. https://doi.org/10.3389/fenvs.2022.1009433.

[20] Drummond C., & Holte R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65, 95-130. https://doi.org/10.1007/s10994-006-8199-5.

[21] Engeset A. B. (2020). For better or for worse”—The role of family ownership in the resilience of rural hospitality firms. Scandinavian Journal of Hospitality and Tourism, 20, 68-84. https://doi.org/10.1080/15022250.2020.1717600.

[22] Filimonau V., & De Coteau D. (2020). Tourism resilience in the context of integrated destination and disaster management (DM2). International Journal of Tourism Research, 22, 202-222. https://doi.org/10.1002/jtr.2329.

[23] Freeman V. (2023). Production and perception of prevelar merger: Two-dimensional comparisons using Pillai scores and confusion matrices. Journal of Phonetics, 97, a)101213. https://doi.org/10.1016/j.wocn.2023.101213.

[24] Gabriel-Campos E., Werner-Masters K., Cordova-Buiza F., & Paucar-Caceres A. (2021). Community eco-tourism in rural Peru: Resilience and adaptive capacities to the Covid-19 pandemic and climate change. Journal of Hospitality and Tourism Management, 48, 416-427. https://doi.org/10.1016/j.wocn.2023.101213.

[25] Ghorbani H., Krasnikova A., Ghorbani P., Ghorbani S., Hovhannisyan H. S., Minasyan A.,... Azodinia M. (2023a). Improving the estimation of coronary artery disease by classification machine learning algorithm. Paper presented at the 2023 IEEE 6th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE), Budapest, Hungary. https://doi.org/10.1109/CANDO-EPE60507.2023.10418014.

[26] Ghorbani H., Krasnikova A., Ghorbani P., Ghorbani S., Hovhannisyan H. S., Minasyan A.,... Azodinia M. (2023b). Prediction of heart disease based on robust artificial intelligence techniques. Paper presented at the 2023 IEEE 6th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE), Budapest, Hungary. https://doi.org/10.1109/CANDO-EPE60507.2023.10417981.

[27] Gómez López C. S., & Barrón Arreola K. S. (2019). Impacts of tourism and the generation of employment in Mexico. Journal of Tourism Analysis: Revista deAnálisis Turístico, 26, 94-114. https://doi.org/10.1108/jta-10-2018-0029.

[28] Guo Y., Zhang J., Zhang Y., & Zheng C. (2018). Examining the relationship between social capital and community residents’ perceived resilience in tourism destina- tions. Journal of Sustainable Tourism, 26, 973-986. https://doi.org/10.1080/09669582.2018.1428335.

[29] Hari Y., Yanggah M. E., & Paramita A. S. (2025). Assessing novice voter resilience on disinformation during Indonesia elections 2024 with Naïve Bayes classifier. Journal of Applied Data Sciences, 6, 299-310. https://doi.org/10.47738/jads.v6i1.489.

[30] Heckerman D., Mamdani A., & Wellman M. P. (1995). Real-world applications of Bayesian networks. Communications of the ACM, 38, 24-26. https://doi.org/10.1145/203330.203334.

[31] Hsu C. -I., Shih M. -L., Huang B. -W., Lin B. -Y., & Lin C. -N. (2009). Predicting tourism loyalty using an integrated Bayesian network mechanism. Expert Systems with Applications, 36, 11760-11763. https://doi.org/10.1016/j.eswa.2009.04.010.

[32] Hu Z., Chai L., Crow W. T., Liu S., Zhu Z., Zhou J.,... Lu Z. (2022). Applying a wavelet transform technique to optimize general fitting models for SM analysis: A case study in downscaling over the Qinghai-Tibet Plateau. Remote Sensing, 14, 3063. https://doi.org/10.3390/rs14133063.

[33] Jamini D., & Dehghani A. (2022). Evaluation and analysis of resilience of rural tourism and identification of key drivers affecting it in the face of the Covid-19 pandemic in Iran. Journal of Research and Rural Planning, 11, 99-116. https://doi.org/10.22067/JRRP.V11I4.2208.1056.

[34] Jamshidi A., Jamshidi M., & Abadi B. (2022). Determinants of adaptation to climate change: A case study of rice farmers in Western Province, Iran. Chinese Geographical Science, 32, 110-126. https://doi.org/10.1007/s11769-021-1246-0.

[35] Kaastra I., & Boyd M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10, 215-236. https://doi.org/10.1016/0925-2312(95)00039-9.

[36] Khezri S., & Mrowati M. (2015). Evaluation of chemical pollution hazards of karstic water in Qouri-Qalae cave. Environmental Management Hazards, 2,85-104. https://doi.org/10.22059/JHSCI.2015.53923 (In Persian).

[37] Kirasich K., Smith T., & Sadler B. (2018). Random forest vs logistic regression: Binary classification for heterogeneous datasets. SMU Data Science Review, 1, 9. https://scholar.smu.edu/datasciencereview/vol1/iss3/9.

[38] Kohavi R. (1996). Scaling up the accuracy of naive-bayes classifiers:A decision-tree hybrid. In E. Simoudis, J. Han, & U. M. Fayyad (Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (Eds.),KDD-96). AAAI Press.

[39] Kuščer K., Eichelberger S., & Peters M. (2022). Tourism organizations’ responses to the COVID-19 pandemic: An investigation of the lockdown period. Current Issues in Tourism, 25, 247-260. https://doi.org/10.1080/13683500.2021.1928010.

[40] Lee S. -H., Kao H. -T., & Kung P. -C. (2022). Staying at work? The impact of social support on the perception of the COVID-19 epidemic and the mediated moderating effect of career resilience in tourism. Sustainability, 14, 5719. https://doi.org/10.3390/su14095719.

[41] Lew A. A., Ng P. T., Ni C. -c., & Wu T. -c. (2016). Community sustainability and resilience: Similarities, differences and indicators. Tourism Geographies, 18, 18-27. https://doi.org/10.1080/14616688.2015.1122664.

[42] Li J., Gao F., Lin S., Guo M., Li Y., Liu H.,... Wen Q. (2023). Quantum k-fold cross-validation for nearest neighbor classification algorithm. Physica A: Statistical Mechanics and its Applications, 611, 128435. https://doi.org/10.1016/j.physa.2022.128435.

[43] Luo Y., He J., Mou Y., Wang J., & Liu T. (2021). Exploring China’s 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tourism Management Perspectives, 37, 100769. https://doi.org/10.1016/j.tmp.2020.100769.

[44] Manaouch M., Naimi L., Haynou M., Aghad M., Sadiki M., Pham Q. B., & Jakimi A. (2025). Enhancing geotourism in southeastern Morocco through machine learning- based geomorphosite identification. Geoheritage, 17, 34. https://doi.org/10.1007/s12371-025-01076-7.

[45] Marco-Lajara B., Úbeda-García M., Ruiz-Fernández L., Poveda-Pareja E., & Sánchez-García E. (2022). Rural hotel resilience during COVID-19: The crucial role of CSR. Current Issues in Tourism, 25, 1121-1135. https://doi.org/10.1080/13683500.2021.2005551.

[46] Marcot B. G., & Penman T. D. (2019). Advances in Bayesian network modelling: Integration of modelling technologies. Environmental Modelling & Software, 111, 386-393. https://doi.org/10.1016/j.envsoft.2018.09.016.

[47] Markoulidakis I., Kopsiaftis G., Rallis I., & Georgoulas,I (2021). Multi-class confusion matrix reduction method and its application on net promoter score classification prob- lem. Paper presented at the 14th PErvasive Technologies Related to Assistive Environments Conference, Corfu, Greece.

[48] Matei D., Chiriţă V., & Lupchian M. M. (2021). Governance and tourism resilience during the COVID19 crisis. Case study Bukovina, Romania. Geo Journal of Tourism and Geosites, 34, 256-262. https://doi.org/10.30892/gtg.34135-646.

[49] Matshusa K., Llewellyn L., & Thomas P. (2021). The contribution of geotourism to social sustainability: Missed opportunity? The International Journal of Social Sustainability in Economic, Social, and Cultural Context, 17(1), 95-118. https://doi.org/10.18848/2325-1115/cgp/v17i01/95-118.

[50] Mendoza Á. G. F., Mateos M. R., & Reinoso N. G. (2021). Perception and rating of tourism entrepreneurs in the recovery of travel destinations affected by social-natural disasters: Case study from the April 16th earthquake in Ecuador. International Journal of Disaster Risk Reduction, 64, 102488. https://doi.org/10.1016/j.ijdrr.2021.102488.

[51] Murphy K. (2022). Naive Bayes classifiers. Columbia: University of British Columbia.

[52] Ngoc Su D., Luc Tra D., Thi Huynh H. M., Nguyen H. H. T., & O’Mahony B. (2021). Enhancing resilience in the Covid-19 crisis: Lessons from human resource manage- ment practices in Vietnam. Current Issues in Tourism, 24, 3189-3205. https://doi.org/10.1080/13683500.2020.1863930.

[53] Ntounis N., Parker C., Skinner H., Steadman C., & Warnaby G. (2022). Tourism and hospitality industry resilience during the Covid-19 pandemic: Evidence from England. Current Issues in Tourism, 25, 46-59. https://doi.org/10.1080/13683500.2021.1883556.

[54] Ólafsdóttir R. (2019). Geotourism. Geosciences, 9(1), 48. https://doi.org/10.3390/geosciences9010048.

[55] Ólafsdóttir R., & Dowling R. (2014). Geotourism and geoparks—A tool for geoconservation and rural development in vulnerable environments: A case study from Iceland. Geoheritage, 6, 71-87. https://doi.org/10.1007/s12371-013-0095-3.

[56] Pajila P. B., Sheena B. G., Gayathri A., Aswini J., Nalini M., & Siva Subramanian (2023, September). A comprehensive survey on naive bayes algorithm: Advantages, limitations and applications. Trichy, India: IEEE, 1228-1234. https://doi.org/10.1109/ICOSEC58147.2023.10276274.

[57] Palmer A., José Montaño J., & Sesé A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27,781-790. https://doi.org/10.1016/j.tourman.2005.05.006.

[58] Partelow S. (2021). Social capital and community disaster resilience: Post-earthquake tourism recovery on Gili Trawangan, Indonesia. Sustainability Science, 16, 203-220. https://doi.org/10.1007/s11625-020-00854-2.

[59] Pathiranage N. W., Waheduzzaman W., & Dayarathna K. T. (2025). Top management gender diversity and environmental performance: Evidence from hospitality and tourism industry. International Journal of Hospitality Management, 130, 104262. https://doi.org/10.1016/j.ijhm.2025.104262.

[60] Polukhina A., Sheresheva M., Efremova M., Suranova O., Agalakova O., & Antonov-Ovseenko A. (2021). The concept of sustainable rural tourism development in the face of COVID-19 crisis: Evidence from Russia. Journal of Risk and Financial Management, 14, 38. https://doi.org/10.3390/jrfm14010038.

[61] Powell R., Green T., Holladay P., Krafte K., Duda M., Nguyen M.,... Das P. (2018). Examining community resilience to assist in sustainable tourism development planning in Dong Van Karst Plateau Geopark. Vietnam. Tourism Planning & Development, 15, 436-457. https://doi.org/10.1080/21568316.2017.1338202.

[62] Quinlan J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106. https://doi.org/10.1023/A:1022643204877.

[63] Rahman M., Muzareba A. M., Amin S., Faroque A. R., & Gani M. O. (2021). Tourism resilience in the context of tourism destination management in post-COVID-19 Bangladesh. In V. G. B. Gowreesunkar, S. W. Maingi, H. Roy, & R. Micera (Tourism destination management in a post-pandemic context:Eds.), Global issues and des- tination management solutions. Emerald Publishing Limited. https://doi.org/10.1108/978-1-80071-511-020211008.

[64] Saito T., & Rehmsmeier M. (2017). Precrec: Fast and accurate precision-recall and ROC curve calculations in R. Bioinformatics, 33, 145-147. https://doi.org/10.1093/bioinformatics/btw570.

[65] Sari N. M., Nugroho I., Julitasari E. N., & Hanafie R. (2022). The resilience of rural tourism and adjustment measures for surviving the COVID-19 pandemic: Evidence from Bromo Tengger Semeru National Park, Indonesia. Forest and Society, 6, 67-83. https://doi.org/10.24259/fs.v6i1.18054.

[66] Savaiano S., & Drago C. (2021). Cluster validation in unsupervised machine learning with application to the analysis of the tourism demand in Italy after COVID-19 lock- down. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3801106.

[67] Savari M., Yazdanpanah M., & Rouzaneh D. (2022). Factors affecting the implementation of soil conservation practices among Iranian farmers. Scientific Reports, 12, a)8396. https://doi.org/10.1038/s41598-022-12541-6.

[68] Shang H., Su L., Liu Y., Tsangaratos P., Ilia I., Chen W.,... Duan Z. (2025). Assessment of the effects of characterization methods selection on the landslide suscepti- bility: A comparison between logistic regression (LR), naive Bayes (NB) and radial basis function network (RBF network). Bulletin of Engineering Geology and the Environment, 84, 134. https://doi.org/10.1007/s10064-025-04097-2.

[69] Sharma S. K., Metri B., Dwivedi Y. K., & Rana N. P. (2021). Challenges common service centers (CSCs) face in delivering e-government services in rural India. Government Information Quarterly, 38, 101573. https://doi.org/10.1016/j.giq.2021.101573.

[70] Sio-Chong U., & So Y. -C. (2020). The impacts of financial and non-financial crises on tourism: Evidence from Macao and HongKong. Tourism Management Perspectives, 33, 100628. https://doi.org/10.1016/j.tmp.2019.100628.

[71] Soliku O., Kyiire B., Mahama A., & Kubio C. (2021). Tourism amid COVID-19 pandemic: Impacts and implications for building resilience in the eco-tourism sector in Ghana’s Savannah region. Heliyon, 7(9), e07892. doi:10.1016/j.heliyon.2021.e07892..

[72] Sujon K. M., Hassan R., Choi K., & Samad M. A. (2025). Accuracy, precision, recall, F1-score, or MCC? Empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models. Journal of Big Data, 12, 268. https://doi.org/10.1186/s40537-025-01313-4.

[73] 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.

[74] Tehrani F. S., Calvello M., Liu Z., Zhang L., & Lacasse S. (2022). Machine learning and landslide studies: Recent advances and applications. Natural Hazards, 114, 1197-1245. https://doi.org/10.1007/s11069-022-05423-7.

[75] Traskevich A., & Fontanari M. (2023). Tourism potentials in post-COVID19: The concept of destination resilience for advanced sustainable management in tourism. Tourism Planning & Development, 20, 12-36. https://doi.org/10.1080/21568316.2021.1894599.

[76] Vaishar A., & Šťastná M. (2022). Impact of the COVID-19 pandemic on rural tourism in Czechia preliminary considerations. Current Issues in Tourism, 25, 187-191. https://doi.org/10.1080/13683500.2020.1839027.

[77] Vărzaru A. A., Bocean C. G., & Cazacu M. (2021). Rethinking tourism industry in pandemic COVID-19 period. Sustainability, 13, 6956. https://doi.org/10.3390/su13126956.

[78] Wang T., Yang Z., Chen X., & Han F. (2022). Bibliometric analysis and literature review of tourism destination resilience research. International Journal of Environmental Research and Public Health, 19, 5562. https://doi.org/10.3390/ijerph19095562.

[79] Webb G. I., Keogh E., & Miikkulainen R. (2011). Naïve Bayes. InC. Sammut, & G. I. Webb (Eds.), Encyclopedia of machine learning. Boston: Springer. https://doi.org/10.1007/978-0-387-30164-8_576.

[80] Weng L., Wu Y., Han G., Liu H., & Cui F. (2022). Emotional state, psychological resilience, and travel intention to National Forest Park during COVID-19. Forests, 13, 750. https://doi.org/10.3390/f13050750.

[81] Wieczorek-Kosmala M. (2022). A study of the tourism industry’s cash-driven resilience capabilities for responding to the COVID-19 shock. Tourism Management, 88, 104396. https://doi.org/10.1016/j.tourman.2021.104396.

[82] Yesilnacar E., & Topal T. (2005). Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79, 251-266. https://doi.org/10.1016/j.enggeo.2005.02.002.