# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 1 Introduction  
Tailings reservoirs are an indispensable and important part of mining activities and they exist widely. However, there are some potential safety hazards, such as dam break, leakage, environmental pollution, and so on. These hidden dangers not only threaten the balance of the surrounding ecological environment but also pose serious threats to the safety of residents and environmental protection. Once the dam breaks, it will affect the health of residents and pose a huge burden on the economy [1]. Therefore, it is particularly urgent and important to carry out research on risk prediction models for tailings ponds, which is of great significance to people's lives and environmental protection. In recent years, there are many scholars experimenting on risk prediction of various tailings ponds. [PERSON] et al. [2] proposed a combination method of a one-dimensional convolutional neural network (1 DCNN) and long short-term memory neural network (LSTM) to predict the infiltration line. Taking the main dam of the Fengshuigou tailings pond in Qidashan, Liaoning Province, as an example, five main factors, namely, the historical infiltration line, reservoir water level, internal and external displacement of the dam, and length of the dry beach, were used as model input data to predict the location of the infiltration line in the future in 1D and the future in 3D. [PERSON] et al. [3]combined Flow-3D numerical simulation technology to conduct an in-depth study on the evolution process of tailings sand flow after the diffuse roof breach of a tailings pond. [PERSON] et al. [4] proposed a kriging space prediction algorithm based on the particle swarm algorithm to optimise the kriging model to fit the internal parameter data of tailings ponds, aiming to improve the fitting accuracy of the internal parameter information of tailings ponds and reduce the error. [PERSON] et al. [5] constructed a BWOA-SVM model to predict the risk level of tailings ponds. [PERSON] et al. [6] proposed a tailings dam displacement prediction method based on temporal decomposition and the sparrow search algorithm-long and short-term memory-attention mechanism (SSA-LSTM-Attention) model. [PERSON] et al. [7] proposed a method to predict the safety situation of tailing reservoir infiltration line by integrating convolutional neural network (CNN) and gated cycle unit (GRU), so as to grasp the stability and safety development situation of the dam body. [PERSON] et al. [8] proposed to forecast the runoff of irrigated rice fields in southern China based on the EEMD-LSTM model, which also simulated and predicted the multi-factor correlation analysis and achieved good results.Yingkang [PERSON] et al. [9], owing to the nonlinearity and uncertainty of workshop power consumption data, reported that it is very difficult to establish an accurate energy consumption prediction model. They proposed an energy consumption prediction model based on Prophet-EEMD-LSTM and predicted the final energy consumption value using LSTM's excellent prediction performance on time series data. The effect was good. [PERSON] et al. [10] used the coupling model to fit and forecast different data related to precipitation series and used multiple evaluation indicators to evaluate the prediction performance. The experimental results showed that the model had a good effect and had high confidence in future precipitation prediction results. The above research has achieved certain results, but there is still a gap that needs to be supplemented and further studied. For example, the Fengshuigou tailings pond in Qidashan, Liaoning Province, is a valley-type tailings pond, which can be extended by a mountainside tailings pond. Five main factors were used for the analysis and simulation and more related influencing factors can be studied for comparative analysis. For example, more kinds of relevant data, such as the infiltration line, rainfall, reservoir water level, and displacement, are widely used.