Simulation of Environmental Damage Assessment Model in Mining Area Based on Deep Convolutional Neural Network Algorithm

The application of Deep Convolutional Neural Network (DCNN) in the field of environmental damage assessment in mining areas is increasingly attracting attention. This study aims to explore and develop a mining area environmental damage assessment model based on DCNN algorithm, and conduct simulation research. Through the comprehensive utilization of satellite images, geological data and meteorological data, we established a multi-source data-driven Mask R-CNN model to capture the details and trends of environmental changes in mining areas. The research results show that, compared with other Transformer networks, the vegetation identification accuracy of this method is the highest. IoU and F1 reached 94.976% and 96.273% respectively. Compared with traditional methods, the evaluation model based on DCNN realizes a higher degree of automation, thus improving the evaluation efficiency. The research results show that this model shows excellent performance in accuracy and accuracy, and can effectively evaluate the degree of environmental damage in mining areas. The simulation of environmental damage assessment model in mining area based on DCNN provides a new and powerful tool for environmental protection and is expected to play an important role in future mining activities.