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.     

Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery

Frequent human activity and rapid urbanization have led to an assortment of environmental issues. Monitoring land-cover change is critical to efficient environmental management and urban planning. The current study had two objectives. The first was to compare pixel-based random forest (RF) and decision tree (DT) classifier methods and a support vector machine (SVM) algorithm both in pixel-based and object-based approaches for classification of land-cover in a heterogeneous landscape for 2010. The second was to examine spatio-temporal land-cover change over the last two decades (1990–2010) using Landsat data. This study found that the object-based SVM classifier is the most accurate with an overall classification accuracy of 93.54% and a kappa value of 0.88. A post-classification change detection algorithm was used to determine the trend of change between land-cover classes. The most significant change from 1990 to 2010 was caused by the expansion of built-up areas. In addition to the net changes, the rate of annual change for each phenomenon was calculated to obtain a better understanding of the process of change. Between 1990 and 2010, an average of 4.53% of lands turned to the built-up annually and there was an annual decrease of about 0.81% in natural land. If the current trend of change continues, regardless of the actions of sustainable development, drastic declines in natural areas will ensue. The results of this study can be a valuable baseline for land-cover managers in the region to better understand the current situation and adopt appropriate strategies for management of land-cover.