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

• Published in: International journal of environmental research and public health Vol. 17; no. 13; p. 4788
• Main Authors: Ma, Junwei, Liu, Xiao, Niu, Xiaoxu, Wang, Yankun, Wen, Tao, Zhang, Junrong, Zou, Zongxing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2020; MDPI
• Subjects: Artificial neural networks; Back propagation; Canyons; China; Datasets; Displacement; Forecasting; Landslides; Landslides & mudslides; Learning; Learning algorithms; Learning theory; Machine learning; Mathematical models; Neural networks; Parameter uncertainty; Performance prediction; Predictions; Radial basis function; Statistical analysis; Support vector machines; China
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph17134788

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.