Leveraging Recurrent Neural Networks for Lithology Identification and Chinese Rural Landscape Planning in Sustainable Design

This paper explores the integration of ecological sustainability, human-centered design, and advanced computational techniques, with a particular focus on the use of Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and Deep Recurrent Neural Networks (DRNNs) in urban landscape planning and rural landscape restoration. DRNNs, an advanced extension of traditional RNNs, are specifically designed to capture complex temporal dependencies in sequential data through deeper network architectures. These models are particularly effective in identifying intricate patterns in time-series data, making them well-suited for dynamic processes in landscape planning and ecological analysis. The study highlights the significance of applying ecological principles to urban design, aiming to create spaces that are not only visually appealing but also environmentally harmonious and socially inclusive. Additionally, the research investigates the role of installation art in public urban spaces, emphasizing its potential to foster community interaction, raise environmental awareness, and promote sustainability. By integrating data-driven approaches, such as LSTM-based lithology identification and DRNN-based ecological forecasting, the paper illustrates how advanced algorithms can optimize landscape features, predict ecological trends, and guide more informed planning decisions. Ultimately, this research underscores the need for a holistic and sustainable approach in urban landscape design that balances environmental, social, and technological dimensions, ensuring a harmonious coexistence between people and their environments.