Artificial Neural Network-Based Planting Arrangement of Smart City in Green Ecological Environment

Abstract

The traditional urban planting arrangement is largely limited by the designer’s idea and has a high repetition rate and a low reference reuse rate. Therefore, a scientific and reasonable planting arrangement of the urban environment is necessary. In this work, research on planting arrangements in a smart city is carried out under green ecology and environment. Firstly, the planting arrangement is analyzed based on the structure, characteristics, and basic principles of the artificial neural network (ANN) model. ANN is frequently applied in pattern recognition, signal processing, system identification, and optimization. In the field of control, neural networks are used to deal with the nonlinearity and uncertainty of the control system and to approximate the identification function of the system. Secondly, the output value of the planting arrangement in the smart city is calculated according to the error backpropagation algorithm. During this period, the weight is adjusted according to the Hebb criterion, and the relevant statistical model of planting arrangement in the smart city is analyzed by ANN. Finally, suggestions on planting arrangements are given. The research shows that steamed bun-shaped plants have the largest total number in smart cities, followed by spherical and bush-like plants. Planting arrangement for spherical and palm or coconut-form plants is more frequent while planting arrangements for wind-shaped plants have a lower frequency. In terms of the importance of the planting arrangement, these 18 types of plants are very important for the green ecological environment in the smart city. Finally, suggestions on planting arrangements are given according to the research.