A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement
نویسندگان
چکیده
In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, omni-directional prediction is more scientific than single point or interval prediction. this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), Gaussian process regression (GPR) proposed for the point, interval, probabilistic displacement. The model includes two parts: (i) predicting trend through DES-VMD-LSTM; (ii) evaluating uncertainty in first based on GPR model. part, DES used predict displacement, periodic stochastic residual extracted by VMD predicted LSTM. triggering factors are screened maximum information coefficient (MIC), decomposed into low- high-frequency components displacements, respectively. cumulative results achieved adding displacements. By setting as input actual expected output, predictions plausibility validated with data from Bazimen (BZM) Baishuihe (BSH) landslides Three Gorges Reservoir area. This has potential achieve deterministic quantify contained A comparative study shows that method high performance
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ژورنال
عنوان ژورنال: Natural Hazards
سال: 2021
ISSN: ['1573-0840', '0921-030X']
DOI: https://doi.org/10.1007/s11069-021-05104-x