Development of intelligent monitoring system for soil erosion in pipeline engineering based on deep learning

The urbanization process has promoted the construction of pipeline projects, but it has also caused soil erosion problems, posing a threat to the ecological environment and engineering safety. Traditional monitoring methods have the problems of low efficiency, poor accuracy and difficulty in real-time monitoring, so the development of intelligent systems based on deep learning is imminent. The intelligent monitoring system for soil erosion in pipeline projects based on deep learning constructed in this paper has achieved remarkable results. After various tests and verifications, the accuracy of the system reached 94 %, the recall rate was 93 %, the AUC-ROC value was as high as 0.97, and the F1 score was 93.5 %, which far exceeded the traditional monitoring methods. In terms of real-time performance, the data collection cycle is only 3 min, the data processing time is 25 s, the warning response time is 1 min and 30 s, and the total process time is 7 min, which can quickly respond to soil erosion risks. The system is very innovative. With the powerful pattern recognition and data analysis capabilities of deep learning, it breaks through the limitations of traditional methods that rely on manual methods and realizes the intelligence of the entire process from data collection to warning decision-making. Specifically, the study used deep learning technology to collect and integrate multi-source data (including remote sensing images, meteorological data, field sensor data, etc.), and used the CNN-LSTM hybrid model for feature extraction and trend prediction to build a complete intelligent monitoring system. In practical applications, the system can be widely used in various pipeline projects, greatly improving the efficiency and accuracy of soil erosion monitoring, providing key support for ensuring engineering construction safety and maintaining ecological balance, and effectively promoting the process of sustainable development.