David J. Peres

TL;DR: In this article, a Monte Carlo simulation framework is used to estimate landslide-triggering rainfall thresholds, which can be used for early warning in prone areas, by combining stochastic rainfall models and hydrological and slope stability physically based models to generate virtually unlimited-length synthetic rainfall and related slope stability factor.

TL;DR: In this paper, the authors investigated the effect of uncertainty in soil moisture provided by either field sensors or remote sensing on the performance of landslide early warning systems (LEWS) and found that soil moisture information introduced within hydro-meteorological thresholds can significantly reduce the false alarm ratio of LEWS.

TL;DR: In this article, a methodology for estimating the return period of landslide triggering under climate change is proposed, which capitalizes on the combined use of a stochastic rainfall generator and a hydrological and slope stability model.

TL;DR: In this paper, a quantitative analysis of the impacts of uncertain knowledge of landslide initiation instants on the assessment of rainfall intensity-duration landslide early warning thresholds is performed based on a synthetic database of rainfall and landslide information, generated by coupling a stochastic rainfall generator and a physically based hydrological and slope stability model.

TL;DR: In this paper, the authors investigated the use of artificial neural networks (ANNs) fed with reanalysis wind data to extend an observed time series of significant wave heights, considering the influence of the distance of input points and of the number of lags.