A landslide occurs when the balance between a hill’s weight and the countering resistance forces is tipped in favor of gravity. While the physics governing the interplay between these competing forces is fairly well understood, prediction of landslides has been hindered thus far by the lack of field measurements over large temporal and spatial scales necessary to capture the inherent heterogeneity in a landslide.
We propose a network of sensor columns deployed at hills with landslide potential with the purpose if detecting the early signals preceding a catastrophic event. Detection is performed through a three-state algorithm: First, sensors collectively detect small movements consistent with the formation of a slip surface separating the sliding part of hill from the static one. Once the sensors agree on the presence of such a surface, they conduct a distributed voting algorithm to separate the subset of sensors that moved from the static ones. In the second phase, moved sensors self-localize through a trilateration mechanism and their displacements are calculated. Finally, the direction of the displacements as well as the locations of the moved nodes are used to estimate the position of the slip surface. This information along with collected soil measurements (e.g. soil pore pressure) are subsequently passed to a Finite Element Model that predicts whether and when a landslide will occur.
Our initial results from simulated landslides indicate that we can achieve high degree of accuracy (in the order of cm) in the localization step as well as the slip surface estimation step of our algorithm. This accuracy persists as the density and the size of the sensor network decreases as well as when considerable noise is present in the ranging estimates.
- Andreas Terzis, JHU.
- A. Anandarajah, JHU (Civil Engineering).
- I-J.Wang, JHU/APL.
- K. Moore, Colorado School of Mines
- A. Terzis, A. Anandarajah, K. Moore. I-J. Wang, Slip Surface Localization in Wireless Sensor Networks for Landslide Prediction. Appeared in the Proceedings of IPSN 2006. PDF
- A. Terzis, A. Anandarajah, K. Moore, I-J. Wang, Sensor Networks for Landslide Detection. Extended Abstract. Appeared in SenSys 2005. PDF