Modeling and Multi-Objective Optimization of Urban Landscape Ecological Adaptability Based on Digital Twinning

Abstract

In the face of frequent extreme weather and limited architectural space in modern cities, traditional landscape design methods cannot effectively balance ecological performance and public experience. In this study, a digital twin system is established, and quantitative evaluation models of Ecological Adaptation Degree (EAD) and Interactive Experience Utility (IEU) are constructed respectively. Taking a typical public square as an example, the multi-objective optimization is carried out by integrating GIS (Geographic Information System), sensors, and simulation data. Finally, three typical schemes are obtained, revealing the relationship between resource competition and cost constraint among design variables. The results show that the dynamic balance between ecology and experience can be achieved by calculation optimization, which also provides a set of feasible methods for data-driven urban landscape design.