Integrating eco-environment impact and eco-tourism using deep neural network algorithms in the GIoT environment - Li, Hui (Soft computing (Berlin, Germany), 2024)
The incorporation of Internet of Things (IoT) expertise into eco-friendly environments and eco-tourism has profoundly enriched our way of life. The collaboration among the Green Internet of Things (GIoT), big data, and machine learning offers innovative solutions for ecological challenges and sustainable practices. Despite its potential, IoT presents challenges such as heightened energy consumption and electronic waste in ecosystems eco-environments and eco-tourism demand eco-friendliness, prompting a shift to GIoT for a more sustainable tourism sector. This study proposed machine learning techniques within the Green Internet of Things (GIoT), aiming to blend ecological impact assessment with eco-tourism development. It emphasizes handling extensive big data-era datasets via the Deep Neural Network (DNN) algorithm to boost tourism's intelligence, sustainability, and ecological responsibility. The research centers on integrating and expanding eco-environmental impact and eco-tourism enterprises by methodically assessing strategies for enhancing tourism's intelligence, sustainability, and eco-friendliness. It focuses on GIoT's capacity to cater to the tourism sector's needs. Coupled coordination theory is employed to evaluate GIoT's potential in meeting tourism demands, utilizing data from Shaanxi province spanning 2015–2020. The experimental results showed substantial economic and ecological growth in Shaanxi Province, closely linked. The tourism industry's overall index reached 0.90, enhancing industry-environment coordination. The DNN algorithm achieved a remarkable 95% accuracy, surpassing traditional methods. Guanzhong demonstrated the most robust coordination, while southern Shaanxi showed consistently high levels, indicating rapid advancement.