A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.     

Modelling the Mont Terri HE-D experiment for the Thermal–Hydraulic–Mechanical response of a bedded argillaceous formation to heating

Coupled thermal–hydrological–mechanical (THM) processes in the near field of deep geological repositories can influence several safety features of the engineered and geological barriers. Among those features are: the possibility of damage in the host rock, the time for re-saturation of the bentonite, and the perturbations in the hydraulic regime in both the rock and engineered seals. Within the international cooperative code-validation project DECOVALEX-2015, eight research teams developed models to simulate an in situ heater experiment, called HE-D, in Opalinus Clay at the Mont Terri Underground Research Laboratory in Switzerland. The models were developed from the theory of poroelasticity in order to simulate the coupled THM processes that prevailed during the experiment and thereby to characterize the in situ THM properties of Opalinus Clay. The modelling results for the evolution of temperature, pore water pressure, and deformation at different points are consistent among the research teams and compare favourably with the experimental data in terms of trends and absolute values. The models were able to reproduce the main physical processes of the experiment. In particular, most teams simulated temperature and thermally induced pore water pressure well, including spatial variations caused by inherent anisotropy due to bedding.     

Climate change and groundwater resources in Mekong Delta,Vietnam

Groundwater resources have considerable influences on the human population and socioeconomic development of Vietnam and the Mekong River Delta (MRD). This paper presents an overview of the relationship between climate change and groundwater in the MRD, including the challenges, strategies and technical measures. Our results showed that groundwater levels are related to other climate and hydrological variables (i.e., rainfall, river levels, etc.); therefore, the impacts of climate change on the groundwater resources of the Mekong delta are significant, especially on groundwater recharge. Based on the results of this study, it is recommended that groundwater development in the future should focus on reducing groundwater harvesting, enhancing groundwater quantity by establishing artificial works and exploiting surface water. This study suggests that the Artificial Neural Network (ANN) model is an effective tool for forecasting groundwater levels in periods of 1 month and 3 months for aquifers in the natural and tidal regime areas of the delta.     

The roles of the reversibility and irreversibility of capillary bonds on the impact dynamics of agglomerates

Acta Geotechnica - Wet agglomerates such as iron-ores, tablets, and aggregates are omnipresent in multi–field industries. However, our understanding of the impact dynamics of these materials...