Speech-recognition in landslide predictive modelling:A case for a next generation early warning system

Traditional landslide early warnings are based on the notion that intensity-duration relations can be approximated to single precipitation values cumulated over fixed time windows. Here, we take on a similar task being inspired by modeling architectures typical of speech-recognition tasks. We aim at classifying the Turkish landscape into 5 km grids assigned with dynamic landslide susceptibility estimates. We collected all available national information on precipitation-induced landslide occurrences. This information is passed to a Long Short-Term Memory equipped with the whole rainfall time series, obtained from daily CHIRPS data. We test this model: 1) by randomizing the presence/absence data to represent the slope instability over Turkey and over 13 years under consideration (2008–2020) and 2) by assessing the effect of different time windows used to pass the rainfall signal to the neural network. Results show that the inclusion of the full precipitation signal rather than its scalar approximation leads to a substantial increase in prediction power (approximately 20%). This may potentially pave the road for a new generation of speech-recognition-based landslide early warning systems.</p

Traditional and modified Newmark displacement methods after the 2022 Ms 6.8 Luding earthquake (Eastern Tibetan Plateau)

peer reviewedThe Newmark displacement (ND) method, which reproduces the interactions between waves, solids, and fluids during an earthquake, has experienced numerous modifications. We compare the performances of a traditional and a modified version of the ND method through the analysis of co-seismic landslides triggered by the 2022 Ms 6.8 Luding earthquake (Sichuan, China). We implemented 23 ND scenarios with each equation, assuming different landslide depths, as well as various soil-rock geomechanical properties derived from previous studies in regions of similar lithology. These scenarios allowed verifying the presence or absence of such landslides and predict the likely occurrence locations. We evaluated the topographic and slope aspect amplification effects on both equations. The oldest equation has a better landslide predictive ability, as it considers both slope stability and earthquake intensity. Contrarily, the newer version of the ND method has a greater emphasis on slope stability compared to the earthquake intensity and hence tends to give high ND values only when the critical acceleration is weak. The topographic amplification does not improve the predictive capacity of these equations, most likely because few or no massive landslides were triggered from mountain peaks. This approach allows structural, focal mechanism, and site effects to be considered when designing ND models, which could help to explain and predict new landslide distribution patterns such as the abundance of landslides on the NE, E, S, and SE-facing slopes observed in the Luding case

Without power? Landslide inventories in the face of climate change

Projected scenarios of climate change involve general predictions about the likely changes to the magnitude and frequency of landslides, particularly as a consequence of altered precipitation and temperature regimes. Whether such landslide response to contemporary or past climate change may be captured in differing scaling statistics of landslide size distributions and the erosion rates derived thereof remains debated. We test this notion with simple Monte Carlo and bootstrap simulations of statistical models commonly used to characterize empirical landslide size distributions. Our results show that significant changes to total volumes contained in such inventories may be masked by statistically indistinguishable scaling parameters, critically depending on, among others, the size of the largest of landslides recorded. Conversely, comparable model parameter values may obscure significant, i.e. more than twofold, changes to landslide occurrence, and thus inferred rates of hillslope denudation and sediment delivery to drainage networks. A time series of some of Earth's largest mass movements reveals clustering near and partly before the last glacial-interglacial transition and a distinct step-over from white noise to temporal clustering around this period. However, elucidating whether this is a distinct signal of first-order climate-change impact on slope stability or simply coincides with a transition from short-term statistical noise to long-term steady-state conditions remains an important research challenge. Copyright (C) 2011 John Wiley & Sons, Ltd