ABSTRACT
The past 15 years have seen considerable developments in the theory, methods and tools for the assessment of slope instability. The growth in computing technology over this period has allowed workers to develop increasingly sophisticated models and apply these models at increasingly detailed scales, thus allowing greater investigation of a highly dynamic process. However, despite the growth in computing facilities, physically based models of slope stability are still difficult to parameterize, and computationally intensive to run. The difficulties of model parameterization with respect to the accurate determination of location specific estimates of the model's input variables are well recognized (de Roo et al., 1989), and as models become more and more complex they require increasing volumes of data at higher qualities, thus exacerbating the parameterization problem.
