The establishment of monitoring and warning systems for landslide with potential instability risk is an economical and effective way to ensure life safety and reduce property losses. However, as the number of landslides and sensors increases, the amount of monitoring data generated is close to massive, and the traditional monitoring systems are gradually unable to manage and analyze the acquired data. In this study, an intelligent monitoring and early warning system and its application were developed based on microservice architecture. In this system, the functions of landslide’s project management, data reception and parsing, data analysis and early warning are distributed to multiple service instances in the form of microservice, thereby achieving the purpose of decentralizing service pressure. The application results of the system indicated that the microservice architecture could decouple components and balance distributed loads in the system, thereby reducing the difficulties of algorithm development, decentralizing calculation and service pressures and improving the service capacity of the monitoring system.

Along with the development of computer science and communication techniques, tunnel monitoring has been digital and intellectualized gradually. In recent years, traditional tunnel monitoring systems have been becoming powerless to deal with huge amounts of data as the number of tunnels and monitoring sensors surge. In this study, an innovative and comprehensive intelligent tunnel monitoring system was designed based on micro-service architecture. Various function modules were distributed to multiple service instances to get the computing pressure decentralized. The system offered powerful massive data processing support, flexibility, and extensibility. For a certain tunnel, the system could acquire the monitoring data in real-time using wireless sensors deployed on its surface. When data reached the early warning threshold, the system would immediately transmit the warning message to users. Besides, an app was developed to offer support for wireless communication between the monitoring apparatus and watchers. The application of the Anping case, one of the monitoring targets, proved the high efficiency of data transmitting and effective emergency response of the developed system. The results indicated that the microservice architecture could facilitate algorithm development, decentralize calculation pressure, and promote the service capacity by decoupling components and distributing loads.

Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model that combines Variational Mode Decomposition (VMD) and the Long and Short-Term Time-Series Network (LSTNet). The bootstrap algorithm is then used to estimate the Prediction Intervals (PIs) to quantify the uncertainty of the proposed model. First, the cumulative displacements are decomposed into trend displacement, periodic displacement, and random displacement using the VMD with the minimum sample entropy constraint. The feature factors are also decomposed into high-frequency components and low-frequency components. Second, this study uses an improved polynomial function fitting method combining the time window and threshold to predict trend displacement and uses feature factors obtained by grey relational analysis to train the LSTNet networks and predict periodic and random displacements. Finally, the predicted trend, periodic, and random displacement are summed to the predicted cumulative displacement, while the bootstrap algorithm is used to evaluate the PIs of the proposed model at different confidence levels. The proposed model was verified and evaluated by the case of the Baishuihe landslide in the Three Gorges reservoir area of China. The case results show that the proposed model has better point prediction accuracy than the three baseline models of LSSVR, BP, and LSTM, and the reliability and quality of the PIs constructed at 90%, 95%, and 99% confidence levels are also better than those of the baseline models.

The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lishanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides.