# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.1 Analysis of EALL
The specific process is as follows:  
Step 1: Determine the input sequence of the LSTM model and the feature sequence composed of water level, temperature, and aging, and normalize them to construct the input dataset of the LSTM model. Step 2: Determine the parameters of the LSTM model. Set up the LSTM's parameters, setting \(num\) to 3, \(\eta\) to 0.001, and training step size to 10. The original random gradient descent technique is also replaced with the Adam algorithm. The training parameters for the LSTM model are determined with \(Batch\_size\) sets to 10 and \(epoch\) sets to 200. If it is too small, the network will be underfitted, while if it is too large, the network will be overfitted. The model's batch size is a crucial parameter that influences the model's training time and the speed at which the parameters are updated. Step 3: LSTM realizes prediction. According to the parameters determined in the previous two steps, the LSTM model is constructed and trained to obtain the prediction results of the IMF3 sequence. Similarly, the LSTM prediction result of \(imf_{1}(t),imf_{2}(t),\ldots,imf_{5}(t)\) can be obtained. The above steps are performed on the sequences \(imf_{1}(t),imf_{2}(t),\ldots,imf_{9}(t)\) and are predicted by ARIMA model and LSTM model, respectively, and the results obtained are shown in Figure 7 after integration. Figure 7: Integration of forecast results of different IMF sequences. As can be seen from Figure 7, different models are selected to predict different sequences after the original dam data are decomposed by EEMD and have a good performance. In order to further judge the prediction effect of the model, RMSE, MAPE, and MAE are used to evaluate each sequence, and the results are shown in Table 2.  
As can be seen from Table 2, ARIMA and LSTM models are used to predict the original sequence after decomposition, and the overall performance is better, which gives full play to the advantages of the respective models and reduces the error of prediction. (5) LSTM builds the final prediction result. According to the idea of combined model and the prediction process of LSTM in the previous step, the second LSTM model is constructed. The input training data are the linear ARIMA prediction results, \(\hat{L}_{6},\hat{L}_{7},\ldots,\hat{L}_{9}\) of low-frequency sequences, the LSTM prediction results of high-frequency sequences \(\hat{N}_{1},\hat{N}_{2},\ldots\hat{N}_{5}\), and the original sequence \(x_{t}\). The historical relationship between them was learned through LSTM model, then the final predicted value \(y_{t}\) was obtained. Parameters of the LSTM model were initialized, trained, and predicted, and the final prediction result of the deformation sequence \(x_{t}\) was obtained, as shown in Figure 8a. In order to evaluate the feasibility and prediction effect of this model more reasonably and objectively, the ARIMA model, LSTM model, and ARIMA-LSTM model are respectively used to predict the selected dam deformation sequence, and the prediction results of the four models are compared and analyzed with the EALL model, as shown in Figure 8b. As seen in Figure 8b, single-point prediction models like ARIMA and LSTM lack the ability to account for both linear and nonlinear components in the deformation sequence, resulting in a substantial error in the prediction result. The ARIMA-LSTM model adopts a combination model format, and the prediction result is obviously superior to that of a single-point prediction model.