Evaluation method of rationality of urban landscape facility design based on neural network

Urban landscape facilities not only enhance the quality of life for residents and promote social interaction but also add bright elements to the display of urban culture and image. Therefore, this study formulates a scientific and reasonable evaluation index system for the rationality of urban landscape facility design and uses a neural network to carry out intelligent evaluation, in order to obtain optimal evaluation outcomes. The simulation results presented in the research demonstrate the effectiveness of the proposed technique in evaluating the soundness of landscape facilities. Specifically, our work encompasses the following key aspects: 1) Introducing the relevant theoretical knowledge and research progress in urban landscape facility design. 2) Elucidating the basic principles and structures of the backpropagation neural network (BPNN), along with the proposal of an improved genetic algorithm-back propagation neural network (GA-BP) to address the limitations of BPNN. 3) Conducting experiments to determine the optimal parameters for the GA-BP model once training is finished. The trained model is then tested with the experimental data, and the results are compared to those obtained by expert reviews. The experimental findings prove that the GA-BP model is effective in evaluating urban landscape facilities and it holds certain reference and application value for research in urban planning, architecture, landscape, and other related fields.