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Landslide displacement prediction based on multi-source data fusion and sensitivity states
Abstract Owing to the complexity of the coupling relationship between multiple external triggering factors and the internal sensitivity state of landslides, it is difficult to accurately predict the displacement response of landslides to triggering factors, using existing methods. To overcome this setback, two new concepts, namely the trend sequence and sensitivity states, were introduced to quantificationally characterize landslide displacement caused by external factors and the landslide internal states, respectively. The support vector regression method was used to predict the trend sequence, while the long short-term memory neural network was employed to predict the sensitivity state. Thereafter, by fusing the predicted trend sequence and the sensitivity state, a non-linear model for landslide displacement prediction was proposed; moreover, with the Baishuihe landslide, located in the Three Gorges Reservoir area, as a case study, the proposed model was evaluated and validated using a large quantity of rainfall, reservoir water level, and displacement monitoring data, spanning a period of over 11 years. Based on the results obtained, the performance of the proposed model with respect to landslide displacement prediction was satisfactory. Furthermore, compared with three existing traditional prediction models of landslide displacement, the proposed model achieved a higher accuracy. Therefore, this study is helpful because it provides new insights that can be used to develop deep data-mining approaches for landslide displacement prediction.
Highlights Trend sequence and sensitivity states are introduced to predict landslide displacement. A new prediction model is proposed by fusing the trend sequence and the sensitivity states. The proposed model is evaluated by the Baishuihe landslide with using monitoring data spanning over 11 years. The new model has satisfactory performance in predicting landslide displacement.
Landslide displacement prediction based on multi-source data fusion and sensitivity states
Abstract Owing to the complexity of the coupling relationship between multiple external triggering factors and the internal sensitivity state of landslides, it is difficult to accurately predict the displacement response of landslides to triggering factors, using existing methods. To overcome this setback, two new concepts, namely the trend sequence and sensitivity states, were introduced to quantificationally characterize landslide displacement caused by external factors and the landslide internal states, respectively. The support vector regression method was used to predict the trend sequence, while the long short-term memory neural network was employed to predict the sensitivity state. Thereafter, by fusing the predicted trend sequence and the sensitivity state, a non-linear model for landslide displacement prediction was proposed; moreover, with the Baishuihe landslide, located in the Three Gorges Reservoir area, as a case study, the proposed model was evaluated and validated using a large quantity of rainfall, reservoir water level, and displacement monitoring data, spanning a period of over 11 years. Based on the results obtained, the performance of the proposed model with respect to landslide displacement prediction was satisfactory. Furthermore, compared with three existing traditional prediction models of landslide displacement, the proposed model achieved a higher accuracy. Therefore, this study is helpful because it provides new insights that can be used to develop deep data-mining approaches for landslide displacement prediction.
Highlights Trend sequence and sensitivity states are introduced to predict landslide displacement. A new prediction model is proposed by fusing the trend sequence and the sensitivity states. The proposed model is evaluated by the Baishuihe landslide with using monitoring data spanning over 11 years. The new model has satisfactory performance in predicting landslide displacement.
Landslide displacement prediction based on multi-source data fusion and sensitivity states
Liu, Yong (author) / Xu, Chang (author) / Huang, Biao (author) / Ren, Xingwei (author) / Liu, Chuanqi (author) / Hu, Baodan (author) / Chen, Zhe (author)
Engineering Geology ; 271
2020-03-23
Article (Journal)
Electronic Resource
English
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