Interval estimation of landslide displacement prediction is crucial for the landslide early warning. To improve the accuracy of landslide displacement prediction, a dynamic prediction model based on double moving average (DMA) method and long short-term memory (LSTM) network is proposed. The DMA method is used to decompose the cumulative displacement of landslide into trend and periodic displacements, while the LSTM network is used to predict the trend and periodic displacements. The predicted values of cumulative displacement are obtained by reconstructing the predicted trend and periodic displacements. To quantify the uncertainties associated with the predicted values, the prediction intervals are derived according to the probability estimation theory. The proposed interval estimation method was validated on the Baishuihe landslide in the Three Gorges Reservoir area of China. The results show that the LSTM network performs better than the support vector machine (SVM) and Elman network, while the DMA decomposition method outperforms the single moving average (SMA) method. Besides, the proposed estimation method can construct high-quality prediction intervals.

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

A landslide is a type of natural disaster that has the highest frequency, the widest distribution and the heaviest losses worldwide; landslides seriously threaten human life and property and major engineering facilities. Therefore, it is important to improve landslide displacement prediction technology to avoid and mitigate landslide disasters. A landslide displacement prediction method based on a chaotic Gaussian mutation sparrow search algorithm-optimised BP neural network (CG-SSA-BP) is proposed to address the problems of the traditional sparrow search algorithm (SSA)-optimised BP (SSA-BP) neural network; it tends to fall into local optima, and it has slow convergence and a low prediction accuracy for landslide displacement prediction. This paper takes the Baishui River landslide in the Three Gorges reservoir area as the research object, and the double exponential smoothing (DES) method is used to decompose the landslide displacement into a trend term and a periodic term to solve the nonlinear landslide system problem. The results show that the prediction model based on CG-SSA-BP has a better prediction accuracy and better stability compared with the model based on SSA-BP.