# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 5 Conclusions  
In summary, considering the complex coupling between the risk factors of tailings pond dam failure and the problem that LSTM easily loses part of the information in long time series prediction, this paper innovatively proposes a tailings pond dam failure risk prediction method based on EEMD-DA-LSTM by improving EEMD-LSTM. The experiment proves that the EEMD-DA-LSTM model constructed in this paper is more effective in predicting the dam failure risk of tailings pond WKB-1 and tailings pond WKB-2 compared with other models, which proves that the dam failure risk prediction method proposed in this chapter is not only applicable to valley-type tailings ponds, but also applicable to tailings ponds on the sides of mountains. Dual attention can quantify the weights of the input features and explore the dependence relationship between the historical time series information of the tailings pond dam failure risk factors, which achieves better performance in tailings pond dam failure risk prediction. The combined EEMD-DA-LSTM prediction model takes into full consideration the different impacts of multi-factors on tailings pond dam failure and the problem that the LSTM is prone to losing part of the information in long time series prediction, which is a good solution to the problem. The prediction error of strongly nonlinear tailings dam deformation data is small. It is proved through experiments that the method proposed in this paper is suitable for the prediction of tailings pond dam failure risk changes with complex and strong nonlinear characteristics and it is suitable for the prediction of tailings pond dam failure risk changes.  
This paper has reference value for practitioners in the mining industry and environmentally sustainable development. Tailings ponds are related to the mining industry and early warning of tailings pond dam break risk is conducive to environmentally sustainable development. This paper proposes that the model achieves good prediction results in short-term prediction, but it may reduce the model training speed due to the increase in the amount of data. In the future, studying the method of parallelisation of the model will be considered to improve the running speed of the model. This model is feasible for predicting the risk of tailings pond failure in the mining field and in the future, we will consider studying the feasibility of this method in the deformation prediction of reservoir dams or landslide prediction.  
Conceptualization, B.M.; Methodology, J.Z.; Software, J.Z.; Validation, J.Z.; Data curation, C.Z.; Writing--original draft, J.Z.; Writing--review & editing, B.M.; Visualization, C.Z.; Supervision, B.M.; Project administration, B.M. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Natural Science Foundation of China (51804018); State Administration of Work Safety Key Technology Project for Prevention of Major Work Safety Accidents in 2018 (henan-0004-2018 AQ) and the Key Research Project of Henan University (22B430019).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Henan Huirong Oil & Gas Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.