Information-based system identification for predicting the groundwater-level fluctuations of hillslopes

The analysis of pre-existing landslides and landslide-prone hillslopes requires an estimation of maximum groundwater levels. Rapid increase in groundwater levels may be a dominant factor for evaluating the occurrence of landslides. System identification—use of mathematical tools and algorithms for building dynamic models from measured data—is adopted in this study. The fluid mass-balance equation is used to model groundwater-level fluctuations, and the model is analytically solved using the finite-difference method. Entropy-based classification (EBC) is used as a data-mining technique to identify the appropriate ranges of influencing variables. The landslide area at Wushe Reservoir, Nantou County, Taiwan, is chosen as a field test site for verification. The study generated 65,535 sets of numbers for the groundwater-level variables of the governing equation, which is judged by root mean square errors. By applying cross-validation methods and EBC, limited numbers of validation samples are used to find the range of each parameter. For these ranges, a heuristic method is employed to find the best results of each parameter for the prediction model of groundwater level. The ranges for governing factors are evaluated and the resulting performance is examined.