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- W3106254906 abstract "Landslides usually cause not only severe economic impacts in industrial and agricultural fields, but also human toll and property losses. Although many efforts have been devoted into landslides research, identifying effective predictors is still a long-term problem. Previous research mainly focused on monitoring landslides by including all available factors to construct models with high prediction accuracy. However, they did not consider model efficiency by selecting effective variables and none of them aimed to detect which particular factors should be focused on in landslides monitoring. This paper proposes a novel hybrid recurrent method based on Long Short-Term Memory (LSTM) model, ANOVA, and stepwise algorithm to identify which meteorological attributes and soil properties affect soil displacement significantly. Due to the difficulty in accessing landslide monitoring data, soil displacement, which can be treated as a warning sign of landslide if it is accumulated to a certain level, is used in this research instead. Starting with a base model to predict soil displacement time series by conducting a LSTM model with revised loss function, each candidate variable is added to the base model separately and the optimal model in this iteration is selected with the lowest mean squared error. Then, ANOVA is used to evaluate whether the current optimal model improves the soil displacement time series prediction significantly, compared to the base model of the previous iteration. Results of our experiments demonstrate that soil moisture content at 10 cm, 20 cm, and 30 cm depths, and atmospheric pressure are selected as the most significant factors affecting soil displacement prediction." @default.
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- W3106254906 date "2020-11-17" @default.
- W3106254906 modified "2023-09-27" @default.
- W3106254906 title "A Hybrid Recurrent Model Based on LSTM and Statistical Evaluation to Predict Soil Displacement for Landslide Monitoring" @default.
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- W3106254906 doi "https://doi.org/10.1007/978-981-15-8603-3_27" @default.
- W3106254906 hasPublicationYear "2020" @default.
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