Defining extensive patterns of nonstationary deformation by analyzing the physical significance and types of monitor time series

aiming at the lower reliability of landslide early-warning due to the key information data

reflected in stages

repeatability and mutability of landslide process

are limited or uncompleted observed. A method

added or imposed extensive pattern is identified according to current status of landslide

such as deformation stage analyzed use factors superposition technique and the types of time series

and the potential causative factors of prediction time into model of support vector machine (SVM) algorithm is presented. The efficiencies of new idea are tested by predicting two groups of displacement time series. All experimental results show that the project prior knowledge can be availably used to increase data message of nonstationary deformation and guide forecast modeling. In contrast to common SVM

the model based on extensive pattern is more reliable and accurate. Particularly

the average relative error is markedly cut down about 2 to 3 times.