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Balancing Development and Sustainability: A Multilayered Machine Learning Approach to Modelling Complex Tourism Ecosystems

Balancing Development and Sustainability: A Multilayered Machine Learning Approach to Modelling Complex Tourism Ecosystems

Veena Tewari, Swapnil Morande, Amitabh Mishra, Mitra Amini, Kanwal Gul, Shaik Mastan Vali
Copyright: © 2024 |Pages: 15
ISBN13: 9798369333907|ISBN13 Softcover: 9798369344460|EISBN13: 9798369333914
DOI: 10.4018/979-8-3693-3390-7.ch003
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MLA

Tewari, Veena, et al. "Balancing Development and Sustainability: A Multilayered Machine Learning Approach to Modelling Complex Tourism Ecosystems." Achieving Sustainable Transformation in Tourism and Hospitality Sectors, edited by Pankaj Kumar, et al., IGI Global, 2024, pp. 36-50. https://doi.org/10.4018/979-8-3693-3390-7.ch003

APA

Tewari, V., Morande, S., Mishra, A., Amini, M., Gul, K., & Vali, S. M. (2024). Balancing Development and Sustainability: A Multilayered Machine Learning Approach to Modelling Complex Tourism Ecosystems. In P. Kumar, S. Gupta, M. Korstanje, P. Rout, & Madhurima (Eds.), Achieving Sustainable Transformation in Tourism and Hospitality Sectors (pp. 36-50). IGI Global. https://doi.org/10.4018/979-8-3693-3390-7.ch003

Chicago

Tewari, Veena, et al. "Balancing Development and Sustainability: A Multilayered Machine Learning Approach to Modelling Complex Tourism Ecosystems." In Achieving Sustainable Transformation in Tourism and Hospitality Sectors, edited by Pankaj Kumar, et al., 36-50. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3390-7.ch003

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Abstract

Forecasting future trends in tourism growth is imperative for sustainability planning, yet highly complex due to the sector's multifaceted nature. This study leverages machine learning techniques to develop an integrated model predicting foreign tourist arrivals to India. Utilizing 2000-2022 data encompassing tourist statistics alongside relevant socioeconomic indicators, advanced algorithms like XGBoost uncover key drivers and relationships to generate strategic long-range forecasts. The multilayered analysis reveals tourism infrastructure investments strongly stimulate arrivals, underscoring policy priorities. However, skills training expenditures exhibit a more nuanced linkage, indicating localized needs. Beyond forecasting accuracy, the research makes significant methodological contributions regarding multivariate input features and model robustness for tourism ecosystems. It advocates systems thinking-based approaches over reductionist modeling of isolated past arrivals, given tourism's interdependence with broader socioeconomics.

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