Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces

Abstract

Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the fields of urban planning and environmental management. This complexity arises from two main challenges: the limitations in acquiring high-density, high-precision data, and the difficulties traditional methods face in capturing and modeling the complex nonlinear relationships between environmental factors and plant growth. In light of the superior spatial interpolation capabilities of CEDGAN (conditional encoder–decoder generative adversarial neural network), notwithstanding its comparative lack of robustness across different subjects, and the excellent ability of FCNN (fully connected neural network) to fit multiple nonlinear equation models, we have developed two models based on these network structures. One model performs high-precision spatial attribute interpolation for urban green spaces, and the other predicts and evaluates the environmental conditions for plant growth within these areas. Our research has demonstrated that, following training with various samples, the CEDGAN network exhibits satisfactory performance in interpolating soil pH values, with an average pixel error below 0.03. This accuracy in predicting both spatial distribution and feature aspects improves with the increase in sample size and the number of controlled sampling points, offering an advanced method for high-precision spatial attribute interpolation in the planning and routine management of urban green spaces. Similarly, FCNN has shown commendable performance in predicting and evaluating plant growth environments, with prediction errors generally less than 0.1. Comparing different network structures, models with fewer hidden layers and nodes yielded superior training outcomes.