A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Fu, Xiaolin

doi:10.1038/s41598-018-25567-6

Cited by 39 publications

(32 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is recognized that the model having lower values of RMSE and MAE implies better prediction performance. For more information about them, see the literature [30], [31]. In this paper, we also adopt these two indicators to evaluate our model, and the RMSE and MAE can be expressed as…”

Section: B Prediction Processmentioning

confidence: 99%

Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

2020

View full text Add to dashboard Cite

Interval estimation of landslide displacement prediction is significant for landslide early warning. The goal of this paper is to improve the accuracy of landslide displacement point prediction and quantify the uncertainties associated with the predicted values. To do so, a coupling prediction model based on double moving average (DMA) method and long short-term memory (LSTM) network is investigated. The DMA method is employed to decompose cumulative displacement of landslide into trend and periodic displacements, while the LSTM network is adopted to model and predict these two sub displacements. The sum of predicted sub displacements is considered as predicted cumulative displacement. Further, the probability estimation theory is utilized to derive confidence intervals that quantify the uncertainties of the point prediction. The proposed approach was validated on Baishuihe landslide in Three Gorges Reservoir area of China. Results show that the LSTM network performs better than support vector machine and Elman network, while the DMA decomposition method outperforms single moving average method. As a consequence, the coupling prediction model of DMA and LSTM network is a better solution for the point prediction of landslide displacement. Furthermore, the proposed probability estimation method can construct high-quality confidence intervals. INDEX TERMS Landslide displacement prediction, interval estimation, deep learning, time series decomposition, Three Gorges Reservoir.

show abstract

Section: B Prediction Processmentioning

confidence: 99%

Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

2020

View full text Add to dashboard Cite

show abstract

“…However, the simulation technique is always complicated, and it requires accurate soil parameters, which are difficult to obtain. Recently, with the development of machine learning (ML) technique, numerous ML models, including hybrid, ensemble, deep learning, etc., have been widely utilized in geology and environment studies [19][20][21]. Choubin et al applied simulated annealing feature selection to identify key features, and five ML models were used to predict the earth's fissure hazard [22].…”

Section: Introductionmentioning

confidence: 99%

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Cao

Yin

Zhou

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.

show abstract

“…Monitoring and early warning (EW) systems for the detection of natural hazards are usually based on a network of sensors and related to developments in geospatial engineering (Bhattacharya et al, 2012), which enable the protection of infrastructure and populations, and the mitigation of long-term consequences (Kubo et al, 2011). The developments of GPS technology, that is, sampling rate up to 100 Hz (Häberling et al, 2015;Zhou et al, 2018), the introduction of additional satellite systems as GLONASS, BeiDou, Galileo, and so forth (Msaewe et al, 2017;Teunissen et al, 2014), and the broad operation of permanent GNSS networks (Bock & Melgar, 2016) provide continuous time series of GNSS products, which can reflect potential ground deformation (Liu et al, 2017;Reilinger et al, 2006) and troposphere/ionosphere abnormalities (Wielgosz et al, 2005), all of which are related to geohazards. More specifically, GNSS coordinate time series are used in applications for the estimation of earthquake magnitude (Blewitt et al, 2006;Wright et al, 2012) and earthquake characteristics (Geng et al, 2013;Melgar et al, 2013;Psimoulis et al, 2014), tsunamis (Ohta et al, 2012), hydrological loadings (van Dam et al, 2001;Tregoning et al, 2009), vertical land movements and sea-level rise (Teferle et al, 2006), and ionospheric storms (Wielgosz et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…), and the seismic wave detection (Pérez-Campos et al, 2013) or to predict the generation of tsunamis (Blewitt et al, 2009). A similar approach is also followed for the application of GNSS networks in the monitoring of landslides and the modeling of ground motion (Benoit et al, 2015;Zhou et al, 2018). Furthermore, recent studies have revealed the potential contribution of high-rate GNSS data in earthquake early warning systems by estimating the predominant period (Psimoulis, Houlié, & Behr, 2018) and the peak displacement (Crowell et al, 2016) of the P-waves, supplementing the current seismic data-based early warning techniques.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Algorithm Approach to the Analysis of GNSS Coordinate Time Series for Detecting Geohazards and Anomalies

et al. 2020

View full text Add to dashboard Cite

In this study, a multiple algorithm approach to the analysis of GNSS coordinate time series for detecting geohazards and anomalies is proposed. This multiple algorithm approach includes the novel use of spatial and temporal analyses. In the spatial analysis algorithm, the spatial autoregressive model was used, assuming that the GNSS coordinate time series from a network of stations are spatially dependent. Whereas in the temporal analysis algorithm, it is assumed that the GNSS coordinate time series of a single station is temporally dependent and an artificial neural network is used to extract this dependency as a nonparametric model. This multiple algorithm approach was examined using (i) the BIGF network of GNSS stations in the British Isles and (ii) the GNSS stations of the GEONET network in Japan for the Tohoku‐Oki 2011 Mw9.0 earthquake. It was demonstrated in these case studies that this multiple algorithm approach can be used to detect the effect of a geohazard such as an earthquake on the GNSS network coordinate time series and to detect regional anomalies in the GNSS coordinate time series of a network. The spatial analysis algorithm seemed to be more suitable to detect coordinate offsets in the low‐frequency component and/or variations in the long‐term trends of the GNSS coordinate time series, while it is less reliable in detecting sudden large magnitude coordinate offsets due to earthquakes, as the effects at one station propagate to nearby stations. In contrast, the temporal analysis algorithm detects coordinate offsets in the high‐frequency component which makes it effective in detecting sudden large coordinate offsets in the GNSS coordinate time series such as those due to earthquakes. Thus, it was shown the complementary of the temporal and spatial analysis algorithms and their successful application for the magnitude and frequency content of the anomalies in the two case studies.

show abstract

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Cited by 39 publications

References 49 publications

Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

A Multiple Algorithm Approach to the Analysis of GNSS Coordinate Time Series for Detecting Geohazards and Anomalies

Contact Info

Product

Resources

About