Methodology for forecasting changes in tourist flow in conditions of emergency situations using remote sensing technologies

M. Kazaryan; M. Shakhramanian; E. Semenishchev

doi:10.1117/12.3017671

7 June 2024 Methodology for forecasting changes in tourist flow in conditions of emergency situations using remote sensing technologies

M. Kazaryan, M. Shakhramanian, E. Semenishchev

Author Affiliations +

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310V (2024) https://doi.org/10.1117/12.3017671
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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Abstract

The article presents methodological approaches to assessing the expected socio-economic effect of reducing the resource potential of the tourism business in conditions of increased possibility of natural and artificial emergencies. The research aims to use Earth remote sensing technologies in recreational areas with subsequent socio-economic analysis to reduce the resource potential in the tourism business in the context of the deteriorating ecology of the territories. The work uses regression analysis, methods for deciphering satellite images using various techniques, mathematical modelling and the formation of forecasts using regression models. A general methodology is proposed for constructing a geo-ecological model using information generated from data from space, Earth remote sensing technologies, and monitoring territories for waste sites with a socio-economic justification for tourism business in recreational areas. A mathematical model for the formation of tourist flows using remote sensing technologies has been developed, as well as a methodology for forecasting tourist flows using PLS and PLS-LM methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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M. Kazaryan, M. Shakhramanian, and E. Semenishchev "Methodology for forecasting changes in tourist flow in conditions of emergency situations using remote sensing technologies", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310V (7 June 2024); https://doi.org/10.1117/12.3017671

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