Abstract
Subject review The developmental tendencies of cumulative displacement time series associated with reservoir landslides influenced by large water reservoirs must be effectively predicted. However, traditional methods do not encompass the dynamic response relationships between landslide deformation and its influencing factors. Therefore, a new approach based on the exponential smoothing (ES) and multivariate extreme learning machine methods was introduced to reveal the influencing factors of landslide deformation and to forecast landslide displacement values. First, the influencing factors of reservoir landslide deformation were analysed. Second, the ES method was used to predict the trend term displacement and obtain the periodic term displacement by determining the trend term from the cumulative displacement. Next, multivariate influencing factors were analysed to explain the periodic term displacement. Then, an extreme learning machine (ELM) model was established to predict the periodic term displacement based on the multivariable analysis of influencing factors. Finally, cumulative displacement prediction values were obtained by adding the trend and periodic displacement prediction values. The Bazimen and Baishuihe landslides in Three Gorges Reservoir Area (TGRA) were selected as case studies. The proposed ES-multivariate ELM (ES-MELM) model was compared to the ES-univariate ELM (ES-ELM) model. The results show that reservoir landslide deformation is mainly influenced by periodic reservoir water level fluctuations and heavy rainfall. Additionally, the proposed model yields more accurate predictions than the ES-ELM model.
Highlights
To meet the needs of flood control and clean energy, many large-scale hydropower projects have been built
Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China) systems, will be used to predict monthly periodic item values if landslide displacement is dependent on influencing factors [23÷25]
The exponential smoothing (ES)-ELM model based on the ES method and uni-variable ELM without considering influencing factors was used for comparison
Summary
To meet the needs of flood control and clean energy, many large-scale hydropower projects have been built. The monthly periodic term values reflect periodic characteristics, which are influenced by external factors such as the reservoir water level and seasonal rainfall. Some displacement prediction models that use decomposition have been proposed based on the influence of rainfall and the reservoir water level on monthly periodic displacement [17, 18]. Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China) systems, will be used to predict monthly periodic item values if landslide displacement is dependent on influencing factors [23÷25]. The monthly periodic term values of cumulative displacement will be predicted by a multivariable ELM model. A hybrid model based on the ES method and multivariate ELM (ES-MELM model) is proposed to discuss the effects of influencing factors on landslide deformation and to predict the cumulative landslide displacement values. The ES-ELM model based on the ES method and uni-variable ELM without considering influencing factors was used for comparison
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