Landslides are a serious geohazard worldwide, causing many casualties and considerable economic losses every year. Rainfall-induced shallow landslides commonly occur in mountainous regions. Many factors affect an area’s susceptibility, such as rainfall, the soil, and the slope. In this paper, the effects of rainfall intensity, rainfall pattern, slope gradient, and soil type on landslide susceptibility are studied. Variables including soil volumetric water content, matrix suction, pore water pressure, and the total stress throughout the rainfall were measured. The results show that, under the experimental conditions of this paper, no landslides occurred on a 5° slope. On a 15° slope, when the rainfall intensity was equal to or less than 80 mm/h with a 1 h duration, landslides also did not happen. With a rainfall intensity of 120 mm/h, the rainfall pattern in which the intensity gradually diminishes could not induce landslides. Compared with fine soils, coarser soils with gravels were found to be prone to landslides. As the volumetric water content rose, the matrix suction declined from the time that the level of infiltration reached the position of the matrix. The pore water pressure and the total stress both changed drastically either immediately before or after the landslide. In addition, the sediment yield depended on the above factors. Steeper slopes, stronger rainfall, and coarser soils were all found to increase the amount of sediment yield.

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction.

Accurate assessment of stage–discharge relationships in open channel flows is important to the design and management of hydraulic structures and engineering. Flow junctions commonly occur at the confluence of natural rivers or streams. The effect of flow junctions on the stage–discharge relationship at mountain river confluences was found by measuring velocity fields and water levels in experimental models. The results show that the backwater and accumulation–separation at flow junctions affect the flow structures and patterns in the channel; also, flow confluences may induce complex flow characteristics of backwater and flow separation at river junctions, indicating potential submerged flooding disasters within the confluence zone. The impacts of flow junctions on the stage–discharge relationship are investigated for two physical confluence models built from river confluence prototype systems in southwest China. The results show that the presence of tributary river inflows tends to increase the water level of the main river. This is important for flood control, flood-risk evaluation and engineering (e.g. hydropower station construction) in mountain rivers. Finally, a comparative quantitative analysis based on flow motion equations is conducted to evaluate the stage–discharge relationship in both uniform and regular confluence systems. The results indicate that more accurate prediction can be made when taking into account the flow non-uniformity induced by flow separation, backwater and distorted bed in the junction region.