Assessment of Water Conservation Function Based on Deep Autoencoder

The accuracy of water conservation assessments is crucial for formulating water resource management and ecological protection policies. However, existing methods overly rely on expert judgment and struggle with precision when handling high-dimensional data. To address this, we propose a deep autoencoder-based method for evaluating water conservation functions. For the Gannan water conservation area, we developed a comprehensive evaluation index system and integrated multisource data to create an ecological dataset. By employing a deep autoencoder model combined with convolutional neural networks and a joint training strategy, we achieved feature extraction and dimensionality reduction, mapping high-dimensional data to a low-dimensional latent space. Subsequently, we used the K-means clustering algorithm to validate the model’s classification performance. The clustering accuracy and FMI based on AE-CNN extracted latent variables were significantly higher than those obtained by clustering the raw data directly. This demonstrates that the model effectively extracts data features and significantly enhances classification accuracy, providing robust support for ecological protection and water management.