Application of integrated model based on EEMD-CNN-LSTM for landslide-displacement prediction

Landslide-displacement prediction is critical when evaluating landslide stability. Despite the achievements of time-series methods based on deep-learning paradigms in predicting landslide displacement, the nonstationary, periodic, and trending characteristics of landslide displacement data cause multivariate predictions of current time-series models to easily overfit. Existing studies primarily focus on improving single models, whereas systematic studies pertaining to multimodel integration methods are scarce. This study aims to develop an integrated model that addresses these challenges and improves prediction accuracy.

Considering the volatility of landslide-displacement data and the combined characteristics of their periodic and trending displacement components, a landslide-displacement prediction model combining isolation forest (IF) anomaly detection, ensemble empirical mode decomposition (EEMD), convolutional neural networks (CNNs), and long short-term memory (LSTM) neural networks is proposed. The stepped Baijiabao landslide in the Three Gorges Reservoir area, which is affected by rainfall, is investigated in this study. First, the IF algorithm is introduced to detect anomalies in the original landslide-displacement data. This enables outliers, which can distort the prediction results, to be identified and excluded. Subsequently, EEMD is adopted to decompose the displacement data into intrinsic mode functions (IMFs), which represent the underlying periodic and trend components. This decomposition allows one to analyze the inherent characteristics of the data more comprehensively. Next, a CNN is employed to capture local periodic and trend patterns within the IMFs. CNNs are particularly effective in recognizing spatial patterns and features, thus rendering them suitable for identifying complex patterns in the displacement data. Finally, the overall displacement is predicted using the LSTM model, which is suitable for accommodating sequential data and capturing long-term dependencies. These techniques are integrated to leverage their respective strengths, thereby improving the prediction accuracy.

The results indicate that the root-mean-square error（RMSE）, mean absolute error（MAE）, absolute percentage error in evaluation (MAPE), and determination coefficient（R²）indices of the EEMD-CNN-LSTM model for predicting the overall landslide displacement under rainfall conditions are 0.4190, 0.3139, 0.2379, and 0.9997, respectively, which signify improvements in the accuracy of the first three evaluation indices by 32.3%, 25.1%, and 7.3%, respectively, compared with those of existing models. This significant improvement demonstrates the model’s robustness in accommodating the complexities of landslide-displacement data under varying conditions. For predictions without rainfall, the RMSE, MAE, MAPE, and R² indices are 0.4302, 0.2908, 0.2431, and 0.9996, respectively, which signify improvements in the accuracy of the first three indices by 31.2%, 31.7%, and 8.7%, respectively, compared with those of existing models. These results highlight the model’s high generalizability across different scenarios, as it can maintain high prediction accuracies regardless of external influencing factors such as rainfall. Compared with conventional LSTM, random forest, and EEMD-LSTM models, the EEMD-CNN-LSTM model offers significant advantages under the influence of rainfall and without rainfall, thus significantly reducing overfitting and improving the prediction accuracy. The hybrid approach effectively captures the intricate patterns in the data, which cannot be achieved by single models.

In summary, the multimodel integration method based on IF anomaly detection, EEMD decomposition, local-feature capturing by the CNN, and overall prediction by LSTM significantly improves the accuracy of landslide-displacement prediction, particularly under the influence of rainfall. The integrated model not only addresses the overfitting issues typically encountered in time-series prediction models but also enhances the model’s robustness and reliability. The combination of IF for anomaly detection ensures that outliers do not skew the prediction results, whereas EEMD facilitates the decomposition of data into meaningful components. The CNN’s ability to capture local patterns, coupled with the LSTM’s strength in modeling long-term dependencies, enables the establishment of a comprehensive framework that can effectively accommodate the complexities of landslide displacement data.[Significance]This study provides an effective multimodel integration method for landslide-displacement prediction, which addresses the overfitting issues in existing models as well as offers substantial scientific significance and practical value. The proposed model’s ability to accurately predict landslide displacement under varying conditions is extremely beneficial to the stability evaluation of landslide-prone areas, thereby contributing to disaster prevention and mitigation efforts. The innovation is based on the systematic integration of anomaly detection, data decomposition, and advanced neural-network techniques, which results in a robust framework that outperforms conventional methods. The findings of this study are applicable to real-world scenarios, thereby enhancing the accuracy and reliability of landslide monitoring systems and supporting informed decision-making in hazard management and infrastructure development.