Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China

Abstract

Urban road green belts, an essential component of Urban Green Space (UGS) planning, are vital in improving the urban environment and protecting public health. This work chooses Long Short-Term Memory (LSTM) to optimize UGS planning and design methods in urban road green belts. Consequently, sensitivity-based self-organizing LSTM shows a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 1.75, 1.12, and 6.06, respectively. These values are superior to those of LSTM, XGBoost, and SVR. Furthermore, we configure three typical plant community models using the improved LSTM model and found that different plant community configurations have distinct effects on reducing PM 2.5 concentrations. The experimental results show that other plant community configuration models have specific effects on reducing PM 2.5 concentrations, and the multi-layered green space with high canopy density in the community has a better impact on PM 2.5 reduction than the single-layer green space model with low canopy density. We also assess the reduction function of green road spaces on PM 2.5, which revealed that under zero pollution or slight pollution (PM 2.5 < 100 μg.m−3), the green space significantly reduces PM 2.5. In UGS planning, the proposed model can help reveal UGS spatial morphology indicators that significantly impact PM 2.5 reduction, thereby facilitating the formulation of appropriate green space planning strategies. The finding will provide primary data for selecting urban road green space plant configuration.