Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models.

Depina, Ivan; Oguz, Emir Ahmet; Thakur, Vikas

doi:10.1016/j.compgeo.2020.103660

Cited by 31 publications

(7 citation statements)

References 32 publications

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“…Landslide disasters occur all over the world, seriously threatening people's lives and economic development [1][2][3]. For example, the landslide disaster that occurred in Wuxi County, Chongqing, China, in June 2022 resulted in two deaths, three missing persons, and enormous economic loss.…”

Section: Introductionmentioning

confidence: 99%

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Xie,

Huang

2024

Mathematics

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Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed based on the decomposition of monitoring data before prediction. Firstly, based on the idea of temporal addition, the sparrow search algorithm (SSA) coupled with the variational modal decomposition (VMD) algorithm is used to decompose the total landslide displacement into trend item, periodic item and random item; then, the displacement values of the subitems are fitted by using the long and short-term memory (LSTM) neural network, and the predicted cumulative landslide displacement is obtained by adding up the predicted values of the three subsequences. Finally, the historical measured data of the Shuping landslide is taken as an example. Considering the effects of seasonal rainfall and reservoir water level rise and fall, the displacement of this landslide is predicted, and the prediction results of other traditional models are compared. The results show that the landslide displacement prediction model of SSA-VMD coupled with LSTM can predict landslide displacement more accurately and capture the characteristics of historical signals, which can be used as a reference for landslide displacement prediction.

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Section: Introductionmentioning

confidence: 99%

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Xie,

Huang

2024

Mathematics

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“…Landslide geological disasters are a serious type of geological disaster that occur worldwide, inducing serious threats and losses to the development of human society. In recent years, under the influence of extreme global climate change, seismic activities, coupled with the rapid development of human engineering activities, have become more intense interferences to the natural environment, directly leading to geological disasters with greater intensity and higher frequency [1,2]. This increases the difficulty of developing landslide disaster reduction strategies [3].…”

Section: Introductionmentioning

confidence: 99%

Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

Lin

Liang

et al. 2022

Mathematics

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In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

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“…Balogun et al [17] used the gray wolf optimization algorithm, the bat algorithm, and the cuckoo algorithm to jointly optimize the support vector machine regression model's parameters, which improved the landslide susceptibility prediction accuracy in western Serbia. Ivan et al [18] employed a statistically calibrated Bayesian framework and introduced an approximate likelihood formulation, leading to the improved prediction accuracy of landslide susceptibility. Guo et al [19] proposed a prediction model of back propagation neural network based on wavelet analysis and a gray wolf optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

Zhang

Yan

2022

IJGI

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Landslide susceptibility prediction has the disadvantages of being challenging to apply to expanding landslide samples and the low accuracy of a subjective random selection of non-landslide samples. Taking Fu’an City, Fujian Province, as an example, a model based on a semi-supervised framework using particle swarm optimization to optimize extreme learning machines (SS-PSO-ELM) is proposed. Based on the landslide samples, a semi-supervised learning framework is constructed through Density Peak Clustering (DPC), Frequency Ratio (FR), and Random Forest (RF) models to expand and divide the landslide sample data. The landslide susceptibility was predicted using high-trust sample data as the input variables of the data-driven model. The results show that the area under the curve (AUC) valued at the SS-PSO-ELM model for landslide susceptibility prediction is 0.893 and the root means square error (RMSE) is 0.370, which is better than ELM and PSO-ELM models without the semi-supervised framework. It shows that the SS-PSO-ELM model is more effective in landslide susceptibility. Thus, it provides a new research idea for predicting landslide susceptibility.

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Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models.

Cited by 31 publications

References 32 publications

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

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