Traditional villages, as repositories of cultural heritage and natural landscapes, have gained increasing prominence in the tourism industry. However, balancing authenticity preservation with visitor satisfaction remains a critical challenge. This study employs a combined Analytic Hierarchy Process (AHP)–Scenic Beauty Estimation (SBE) approach under the theoretical framework of 4E theory (Entertainment, Education, Aesthetics, and Escapism) to comprehensively evaluate visitor satisfaction in traditional village tourism landscapes. Eight nationally designated tourism-oriented traditional villages in Anhua County, China were selected as case studies. Findings from the AHP analysis reveal that aesthetic and escapism experiences are the most influential dimensions in shaping visitor satisfaction, while entertainment and educational experiences, though secondary, remain integral to a well-rounded tourism framework. The SBE evaluation corroborates these results, highlighting that seasonal characteristics, stress relief, and cultural landscape diversity significantly enhance visitor experiences. Conversely, deficiencies were observed in social interactivity, satisfaction with educational experiences, and fulfillment of aesthetic needs, indicating areas for improvement. A strong positive correlation between AHP and SBE scores (Pearson correlation coefficient = 0.867, p < 0.01) underscores the alignment between expert-driven evaluations and visitor perceptions. These insights suggest that integrating expert-based hierarchical analysis with empirical visitor assessments provides a more robust and multidimensional framework for sustainable tourism management. Recommendations include enhancing social interactivity, optimizing educational components, enriching aesthetic experiences, and ensuring the preservation of vernacular landscapes to foster sustainable, experience-driven rural tourism development.

Urban stormwater management systems are increasingly strained by rapid urbanization and climate change, yet existing planning approaches often lack holistic optimization frameworks that account for both green and grey infrastructure (GREI) under uncertain future conditions. This study introduces a multi–objective optimization framework for Grey–Green Infrastructure (GGI), which integrates green infrastructure (GI) with GREI to enhance urban flood resilience, cost efficiency, and adaptability. The framework addresses life cycle cost (LCC), technological resilience (Tech-R), and operational resilience (Oper-R), offering a comprehensive approach to navigating the complexities of urban stormwater management. Key findings reveal that: (1) GGI systems optimized for resilience achieve a 33% improvement in Oper-R, with only a marginal increase in LCC of less than 9%, highlighting their robustness under GREI failure scenarios; (2) the integration of bioretention cells (BCs) and porous pavements (PPs) into GGI increases Tech-R by 7.1%, enhancing soil water retention and permeability, particularly in densely urbanized contexts; and (3) decentralized GGI systems exhibit superior adaptability to extreme weather events, with Design D reducing LCC to USD 53.9 M while maintaining no overflow under a 5–year rainfall event. The framework was validated in Zhujiang New Town, Guangzhou, where optimized GGI designs reduced average pipe diameters and manhole depths by 0.2–0.3 m compared to GREI–only systems, demonstrating both cost and resilience advantages. These findings provide decision–makers with a robust tool for evaluating trade–offs in stormwater infrastructure planning, advancing sustainable urban water management.

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.

Rapid urbanisation in coastal megacities intensifies coupled climate and water-related challenges, including heat stress, ventilation deficits, and increasing sensitivity to hydro-climatic extremes. Urban morphology plays a critical role in regulating these climate–water interactions by shaping airflow, surface heat exchange, and the spatial organisation of green–blue infrastructures. This study develops a Local Climate Zone (LCZ)-conditioned generative modelling framework based on a Conditional Pix2Pix Generative Adversarial Network, using paired LCZ classification maps and urban morphology data derived from six representative cities in the Guangdong–Hong Kong–Macao Greater Bay Area: Guangzhou, Shenzhen, Hong Kong, Macao, Zhuhai, and Dongguan. By integrating remote sensing–derived LCZ classifications with urban morphology data, the proposed framework learns spatial patterns associated with key morphology-related predictors, including building density and compactness, height-related structural intensity, open-space distribution, and the continuity of green–blue and ventilation corridors. The model demonstrates robust performance (SSIM = 0.74, R² = 0.81, PSNR = 15.3 dB) and strong cross-city transferability, accurately reproducing density transitions, ventilation corridors, and green–blue spatial structures relevant to coastal climate and water adaptation. The results highlight the potential of LCZ-informed generative modelling as a scalable decision-support tool for climate–water adaptive urban planning, enabling rapid exploration of morphology configurations that support heat mitigation, ventilation enhancement, and resilient coastal transformation.