A Generative Model-Based Network Framework for Ecological Data Reconstruction

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is used to retain more semantic information to generate features. The model was applied to species in Southern California, USA, citing SWOT analysis data to train the model. Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them, effectively solving the problem of insufficient data collection in development environments. The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) classification assessment commonly used in the environmental data domain. This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.