Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

Abstract

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

Prediction of Landslide Displacement Based on the Combined VMD-Stacked LSTM-TAR Model

“…It is studied as a fluctuating displacement with a seasonal cycle. The landslide displacement affected by external factors is generally decomposed in a time series, and the natural development trend displacement of the landslide body and the fluctuating displacement caused by external environmental factors can be predicted [8,9]. Currently, the methods for decomposing the cumulative displacement of landslides include the moving average [10], exponential smoothing [11], empirical mode decomposition [12], and collective empirical mode decomposition methods [13].…”