Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique

Abstract

<jats:p>Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.</jats:p>

A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area

“…However, due to the nonlinear characteristics of the landslide displacement dataset, the accurate prediction of the landslide occurrence needs a lot of resources and is difficult to implement. Although various methods have been proposed to predict the landslide displacement, the prediction accuracy of these methods is still controversial and uncertainty [ 9 ]. Actually, the high degree of uncertainty in the landslide displacement prediction makes it difficult for any single or specific model to be considered as the most suitable model for all scenarios.…”

“…The proposed method is a data-driven model that is also known as the black-box model with a drawback of only prediction error provided and no information regarding the associated predictive uncertainties. The output of most existing data-driven models is a single estimate of each prediction range, and these single estimates that provide deterministic results are called point predictions [ 9 ]. The uncertainties can affect the accuracy of point estimates and are consisting mainly of parameter uncertainty, model uncertainty, and input uncertainty that could be substantial.…”

“…The surface displacement of the landslide is one of the important information generated during the landslide deformation process and is of great significance to predict the evolution law and development trend of landslides according to the analysis of it [ 5 , 6 , 7 ]. Accurately predicting the surface displacement of the landslide is important and necessary for mastering the evolution stages of landslide and realizing the accurate early warning [ 8 , 9 ].…”

Application of Well Drainage on Treating Seepage-Induced Reservoir Landslides

“…In the normal operation of reservoirs, the storage can be quickly vacated in rainy seasons to prevent flooding, leading to rapid drawdown of reservoir water levels [ 3 ]. In this process, great seepage force pointing to the outside of landslide is produced, resulting in decreased stability and intensifying landslide deformation [ 4 , 5 ].…”

Research in Health-Emergency and Disaster Risk Management and Its Potential Implications in the Post COVID-19 World

“…The study of Zhang et al [ 5 ] shows how multi-disciplinary methodologies might be applied to examine communicable disease spread among close-contacts. Ma et al [ 6 ] proposes a landslide risk prediction approach that might be useful to prevent mortality and morbidity in at-risk communities.…”