Construction of dynamic monitoring and early warning model of water and soil loss based on neural network

Soil erosion poses a significant threat to global ecological balance and sustainable development. Traditional monitoring methods cannot effectively cope with problems such as large spatial and temporal variability and wide monitoring range. With the development of remote sensing and geographic information systems (GIS) technologies and the application of neural networks in environmental science, new opportunities are created to improve the accuracy and efficiency of soil erosion monitoring. This study aims to explore the application of neural networks in soil and water loss monitoring and build a corresponding monitoring and early warning model. By collecting GIS, remote sensing and ground observation data, combined with neural network models, this study developed an advanced dynamic monitoring system for soil and water loss. The results show that the model can accurately predict soil erosion under various conditions and can be used in real-time early warning systems. Finally, this study discusses the performance, limitations and comparison of neural network models with traditional methods in real-world applications and provides directions for future research.