Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

Abstract

Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide’s factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.

Predictive Modeling of Landslide Susceptibility in Soft Soil Canal Regions: A Focus on Early Warning Systems

“…Besides, combined models have been highly successful in fields such as flooding [42], [43] and drought [44], which has led researchers to explore ensemble modeling in landslide prediction as well. Risk analysis and forecast of landslide hazards using LSTM-RNN and DBA-LSTM models based on rainfall changes and water level variations have shown high accuracy [45], [46]. Researchers have used the original sequence of landslide displacement as input for combined machine learning models to predict the movement of landslides in steep slopes [47], [48], [49], [50].…”

Land Subsidence Prediction Model Based on the Long Short-Term Memory Neural Network Optimized Using the Sparrow Search Algorithm

“…If the MSE value is smaller, the variation in the residuals is reduced, making the results of the model more stable. The detailed formulas for the three indicators can be found in Equations ( 10)- (12).…”

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

“…Intelligent algorithms have not only been extensively applied in the analysis and prediction of parameters in dam, pile foundation, and tunnel engineering, but also have seen limited research in slope engineering, ground reinforcement, and foundation scour. Dai, Y. proposed a novel landslide warning method based on DBA-LSTM (displacement back analysis based on long short-term memory networks) [25]. The particle swarm optimization (PSO) algorithm [26] and the ANN optimized by the colonial competitive algorithm (ANN-ICA) [27] have been utilized for the analysis of soil strength or ground deformation [28].…”