Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network

Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.

High-resolution structure-from-motion models covering 160 km-long surface ruptures of the 2021 M_(W)7.4 Madoi earthquake in northern Qinghai-Tibetan Plateau

The May 222021 M_(W)7.4 Madoi,Qinghai,China earthquake presented a rare opportunity to apply the modern unmanned aerial vehicle(UAV)photography method in extreme altitude and weather conditions to image surface ruptures and near-field effects of earthquake-related surface deformations in the remote Tibet.High-resolution aerial photographs were acquired in the days immediately following the mainshock.The complex surface rupture patterns associated with this event were covered comprehensively at 3-6 cm resolution.This effort represents the first time that an earthquake rupture in the interior of the Qinghai-Tibetan Plateau has been fully and systematically captured by such high-resolution imagery,with an unprecedented level of detail,over its entire length.The dataset has proven valuable in documenting subtle and transient rupture features,such as the significant mole-tracks and opening fissures,which were ubiquitous coseismically but degraded during the subsequent summer storm season.Such high-quality imagery also helps to document with high fidelity the fractures of the surface rupture zone(supplements of this paper),the pattern related to how the faults ruptured to the ground surface,and the distribution of off-fault damage.In combination with other ground-based mapping efforts,the data will be analyzed in the following months to better understand the mechanics of earthquake rupture related to the fault zone rheology,rupture dynamics,and frictional properties along with the fault interface.