Integrating SSPs and multi-model ensembles in exploring parameter relationships for vertical urban development using a 3D-UGM: A case study of Wuhan, China

As global urbanization accelerates, it exacerbates sustainability challenges across environmental, economic, and social realms, underscoring the need for sophisticated urban planning models. Here, we introduce a novel 3D Urban Growth Model (3D-UGM) that uniquely integrates vertical structural data, enabling enhanced analysis and prediction of urban development patterns. Our model employs an innovative blend of analytical techniques and scenario-based forecasting, setting a new standard in the field. We report that our horizontal urban growth simulations yield a cell-level mean Kappa value of 0.607 and a pattern-level agreement of 88%. Additionally, our vertical growth predictions, informed by random forest algorithms, achieve a testing R2 of 0.909 and a mean Mean Squared Error (MSE) of 0.239. Projecting future urban growth under various Shared Socioeconomic Pathways (SSPs), our model forecasts a potential increase of up to 20% in vertical development under SSP3 compared to SSP1. These projections offer a detailed glimpse into the diverse potential urban landscapes under differing environmental and economic scenarios. Our research makes a critical contribution to urban planning literature, providing a robust, data-driven foundation that informs sustainable development strategies, crucial for managing the complexities of vertical growth in rapidly urbanizing areas worldwide.