Artificial intelligence assisted intelligent planning framework for environmental restoration of terrestrial ecosystems

The environmental restoration of terrestrial ecosystems helps to protect the natural world and enhances sustainable land resource development. Modern and efficient approaches for the conservation of ecological functions must be established for more severe land degradation. In this paper, artificial intelligence assisted intelligent planning framework has been proposed to manage the environmental restoration of the terrestrial ecosystem. Facilitating balance of ecosystem service provision, demand, and using machine learning to dynamically build Biological Retreat Configuration (BRCs) that helps better to apprehend the influence of urban growth on environment-related procedures. Such factors can be used as a theoretical reference in the combination of commercial development and eco-friendly conservation. The BRC of the metro area of Changsha Zhuzhou Xiangtan (CZX) has been developed in this study to classify ecological sources using the Bayesian network model efficiently. Using the Least Collective Resistance (LCR) model and circuit theory, the environmental passage and environmental strategy points were established. The BRC was developed by integrating seven environmental factors with 35 ecological policy points. The results showed that the supply and demand of organic unit services (EUS) were spatially decoupled with the deterioration in locations with a significant EUS trend. The urban agglomeration's environmental sources and ecological corridors have been primarily located in forests and waters. The terrestrial environmental pathway has been scattered around the outer edge of the region, while the aquatic green corridor has been extended over the whole town. The environmentally sensitive areas were located primarily around the borders of the growing region and the intersections between land development and forest area. Finally, environmental components have been mainly identified in existing zones of biological defense, which support the effectiveness of Machine Learning (ML) in green sources forecasting and offer novel insight into the development of urban BRCs. The proposed approach has proven to be effective for the planning of assessing environmental restoration in terrestrial ecosystems.