Prediction Intervals for Landslide Displacement Based on Switched Neural Networks

Lian, Cheng; Chen, C. L. Philip; Zeng, Zhigang; Yao, Wei; Tang, Huiming

doi:10.1109/tr.2016.2570540

Cited by 35 publications

(9 citation statements)

References 43 publications

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“…To evaluate the prediction performance of the proposed landslide displacement prediction model reasonably, specific performance indices were considered to evaluate the model prediction performance [ 32 , 40 , 41 , 42 , 43 ]. This paper introduces four evaluation indices, namely, the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination R2 (R-square).…”

Section: Methodsmentioning

confidence: 99%

Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model

Lin

Sun

2022

IJERPH

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In recent years, machine learning models facilitated notable performance improvement in landslide displacement prediction. However, most existing prediction models which ignore landslide data at each time can provide a different value and meaning. To analyze and predict landslide displacement better, we propose a dynamic landslide displacement prediction model based on time series analysis and a double-bidirectional long short term memory (Double-BiLSTM) model. First, the cumulative landslide displacement is decomposed into trend and periodic displacement components according to time series analysis via the exponentially weighted moving average (EWMA) method. We consider that trend displacement is mainly influenced by landslide factors, and we apply a BiLSTM model to predict landslide trend displacement. This paper analyzes the internal relationship between rainfall, reservoir level and landslide periodic displacement. We adopt the maximum information coefficient (MIC) method to calculate the correlation between influencing factors and periodic displacement. We employ the BiLSTM model for periodic displacement prediction. Finally, the model is validated against data pertaining to the Baishuihe landslide in the Three Gorges, China. The experimental results and evaluation indicators demonstrate that this method achieves a better prediction performance than the classical prediction methods, and landslide displacement can be effectively predicted.

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Section: Methodsmentioning

confidence: 99%

Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model

Lin

Sun

2022

IJERPH

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“…Of particular mention is the work done by Lian et al [7,8] on landslide displacement prediction using Prediction Intervals (PIs) and an ANN switched prediction method. The authors employed K-means clustering for dividing the landslide data into two classes; namely, a majority class (stationary points) and a minority class (mutational points).…”

Section: Actively Unstable Region Fs<1mentioning

confidence: 99%

“…Then, the corresponding class labels y i , T class = {X i , y i }, can be constructed and classified by Equations (7) and (8). To construct the BPNN models for different data patterns, the calculated FS need to be classified in two subsets, as follows:…”

Section: Switch-based Prediction Model Designmentioning

confidence: 99%

Landslide Prediction with Model Switching

2019

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Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environmental data collected by wireless sensor networks (WSNs). However, environmental data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, people can leave the hazardous areas earlier. A good prediction mechanism is, thus, critical. Currently, a widely-used method is Artificial Neural Networks (ANNs), which give accurate predictions and exhibit high learning ability. Through training, the ANN weight coefficients can be made precise enough such that the network works in analogy to a human brain. However, when there is an imbalanced distribution of data, an ANN will not be able to learn the pattern of the minority class; that is, the class having very few data samples. As a result, the predictions could be inaccurate. To overcome this shortcoming of ANNs, this work proposes a model switching strategy that can choose between different predictors, according to environmental states. In addition, ANN-based error models have also been designed to predict future errors from prediction models and to compensate for these errors in the prediction phase. As a result, our proposed method can improve prediction performance, and the landslide prediction system can give warnings, on average, 44.2 min prior to the occurrence of a landslide.

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“…However, these studies are at the nascent stage and very limited. In [21,27], the bootstrap technique and the ELM method were combined for the interval prediction of landslide displacement. e bootstrap technique is the most frequently used technique for the construction of PIs, and it is easy to implement and quite reliable compared with other approaches.…”

Section: Introductionmentioning

confidence: 99%

Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines

et al. 2020

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Accurate and reliable predictions of landslide displacements are difficult to perform using traditional point prediction approaches due to the associated uncertainty. Prediction intervals are effective tools for quantifying the uncertainty of point predictions by estimating the limit of future landslide displacements. In this paper, under the framework of the original lower upper bound estimation method, a direct interval prediction approach is proposed for landslide displacements based on the least squares support vector machine (LSSVM) and differential search algorithms. Two LSSVM models are directly implemented to generate the interval of future displacements, and the optimal model parameters are derived by the differential search algorithm. The Baishuihe landslide and the Tanjiahe landslide located on the shoreline of the Three Gorges Reservoir, China, are used to test the proposed approach. Compared with other models, the proposed method performed best and presented the smallest coverage width-based criterion values of 0.8927 and 1.0562 at monitoring stations XD01 and ZG118 for the Baishuihe landslide, respectively, and 0.1316 and 0.1191 at monitoring stations ZG289 and ZG287 for the Tanjiahe landslide, respectively. The results indicate that the proposed approach can provide high-quality prediction intervals for landslide displacements in the Three Gorges Reservoir area.

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Prediction Intervals for Landslide Displacement Based on Switched Neural Networks

Cited by 35 publications

References 43 publications

Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model

Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model

Landslide Prediction with Model Switching

Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines

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