## 1 Introduction
The choice is predicated on the ANNs superior ability to handle and model complex relationships within large datasets, thereby facilitating more effective optimization and planning of commercial space distribution. The employment of ANNs supports the creation of more rational, data-driven urban planning processes, ultimately leading to better decision-making by urban planners. The strategic application not only optimizes commercial space allocation but also contributes significantly to the broader goals of sustainable urban development, enhancing the overall functionality and livability of urban environments. Within the vast field of ANNs, there exists a wide variety of models, each tailored to specific types of data and prediction needs. Notable among them are radial basis function (RBF) networks, Elman networks, and backpropagation (BP) neural networks, each of which serves a different purpose and demonstrates unique functionality and plays an important role in urban, social, and engineering fields [28; 29; 30; 31; 32; 33; 34; 35; 36]. RBF networks are a type of neural network that uses radial basis functions as activation functions [37; 38]. They are particularly known for their effectiveness in handling non-linear data separations [39; 40; 41]. RBF networks have been extensively applied in urban planning contexts, particularly in predicting urban commercial and industry land demand [42; 43; 44]. Their ability to interpolate surfaces or spaces makes them exceptionally suitable for modeling complex urban growth patterns, thus aiding in the planning and development of smart cities. Elman networks are well suited to capturing dynamic changes over time, making them ideal for applications involving temporal sequences [45]. In the realm of environmental science, Elman networks have been effectively utilized to predict carbon emissions and monitor changes in average sea levels [46]. Their recurrent nature allows them to perform well in scenarios where data points are temporally connected, providing planners and environmentalists with a powerful tool for assessing long-term environmental impacts and trends [47]. BP networks have gained widespread acclaim for their robustness and adaptability across a diverse array of predictive modeling tasks. These networks are particularly lauded for their layered architecture, which allows for the meticulous modeling of complex relationships between input and output variables. The efficacy of BP networks in error correction via the backpropagation algorithm is unparalleled, rendering them exceedingly proficient in unraveling multifaceted problems in predictive analytics [48; 49]. In the domain of urban planning, the application of BP neural networks has been particularly transformative [50]. Urban planners have harnessed these networks to forecast urban air quality indices with remarkable accuracy, taking into account multifarious variables, such as traffic flow, industrial emissions, and meteorological data [51; 52]. This predictive capacity is instrumental in the development of urban policies aimed at air quality management and the mitigation of health risks associated with airborne pollutants. Furthermore, BP neural networks have been adeptly utilized to assess and predict regional flood hazards. These networks analyze vast datasets, including topographical maps, hydrological patterns, and historical flood data, to predict potential flood zones with high precision [53]. This information is crucial for urban developers and policymakers to plan strategically placed flood defenses, implement effective drainage systems, and delineate land use that minimizes flood risk. BP networks extend to the spatial configuration of urban landscapes as well. By analyzing data on population density, land use, and socioeconomic activities, these networks support the optimization of urban space allocation, facilitating the creation of well-balanced, sustainable urban environments [54].