FORECASTING AIR POLLUTION LEVELS IN TOURIST AREAS IN KAZAKHSTAN USING ARTIFICIAL INTELLIGENCE METHOD

Abstract

This article discusses the use of convolutional neural networks (CNN) to assess air pollution levels in tourist areas and its effects on the tourism industry. Air pollution poses serious challenges to public health and environmental sustainability, especially in regions frequented by tourists. CNN algorithms offer a powerful tool for analyzing air quality based on images collected from a variety of sources, including satellite data, unmanned aerial vehicles and ground-based sensors. By processing and analyzing these images, CNNS can detect pollution hotspots, track pollution sources, and predict air quality trends. The introduction of CNN-based air quality analysis in tourist destinations provides a number of benefits, including the creation of early warning systems, improved planning and management, promotion of sustainable tourism practices and reputation management. However, in order to realize the full potential of CNN algorithms in this context, it is necessary to solve problems such as data availability, model generalization and interpretability. The combined efforts of policy makers, industry stakeholders, and technology experts are essential to make effective use of CNN-based solutions and create safer, healthier, and more sustainable travel experiences.

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