The contribution of Artificial Adaptive System to limit the influence of systematic errors in the definition of the kinematic behavior of an extremely-slow landslide

This paper describes the application of some new mathematical algorithms, developed at Semeion Research Center and based on Artificial Adaptive System (AAS), to the redundant measurements of displacement of an extremely-slow landslide that may be affected by some systematic errors. The main aim is to understand if AAS may overcome their influence in the definition of the landslide kinematic behavior thus being able to use the measurements even though they differ by systematic errors. This would be a particularly good result for the monitoring of extremely-slow landslides that move at displacement rates less than 16 mm/year and can be recognized only with instrumentation, usually of geodetic type for the ground surface and inclinometers for the subsurface. In the short time, displacements are so small that they may include systematic errors of the same order of magnitude that can neither be identified nor reduced. For the monitoring of extremely-slow landslides it is therefore recommended to use redundant measurement systems and check the reliability of data by comparing the displacements. This paper shows how the use of the Artificial Adaptive System may get the information on the landslide kinematic even when there is no agreement between displacements measured with the different techniques. The validation of these results was made by comparing them with the well-known data field and a good agreement was found.