Artificial Intelligence-Enhanced Strategic Planning Framework for Sustainable Rehabilitation of Ecosystems and Tourist Attractions Based on Spatiotemporal Dynamics
Frahman, Nidhi Mishra, Rajender Sharma · Springer Link (Chiba Institute of Technology) · 2026
The development of cities, climate change, and unsustainable activities by humans are all making natural ecosystems and tourist sites worse. This means that new, data-driven techniques are needed for sustainable rehabilitation. Conventional ecological restoration techniques frequently exhibit accuracy, flexibility, and sustained efficacy deficiencies within dynamic ecosystems. To deal with these problems, combining Artificial Intelligence (AI) with spatiotemporal analysis has become a revolutionary way to prepare for the natural world and regulate ecosystems. This study utilized artificial intelligence, unmanned aerial vehicles (UAVs), and deep Q-learning networks (DQNs) to create an effective ecological monitoring and restoration system. The main goal was to create a sustainable ecological restoration system to make the area more attractive to tourists (SRE&TA). The system got real-time tracking data from satellites, UAVs, and sensor networks that were far away. The researchers looked at the restoration projects and data from weather sensors to learn more about the restoration initiatives. The results showed that the AI system could greatly improve the efficiency of resources and environmental monitoring. DQNs automated the sustainable ecological rehabilitation system to improve tourism attraction based on spatiotemporal dynamics (SRE&TA- StD). They did this by creating personalized restoration plans. The suggested method using DQN worked for the SRE&TA-StD tests.