Application of data compression based on AIS to the extraction of landslide anomaly
Chen Ling · Journal of Chengdu University of Technology · 2007
The extraction of anomaly is a very important technique for landslide forecasting owing to its good reflection to landslide.Therefore,how to effectively extract the anomaly really related to landslide from complex data information is a key problem.An applied method——the artificial immune algorithm is proposed for data compression in this paper.In case study,the artificial immune algorithm is used to compress the displacement data of the monitoring point 2,6 and 9 of the Danba landslide in Sichuan,China.And three data sets got rid of the impacts of redundancy and irrelevancy are obtained,whose optimal compression rates are 0.821,0.819,0.829 respectively and fidelity is 0.95.Then,the anomaly recognition method is used to the compressed data,and four obvious anomalies are obtained under the confidence level of 0.95.The anomalies reflecting to landslide are in accordance with the other monitoring information such as acoustic emission of rocks.So,it can be easily concluded that the AIA algorithm could be used to compress the monitoring data with higher data density and lower redundancy and irrelevancy.