CAJ | 학술논문

为解决大数据量下滑坡的位移数值精确预测，采用数据挖掘技术对滑坡多源监测数据进行预处理，进而采取粗糙集理论对输入变量集进行定量评价、约减并完成滑坡变形阶段预测，在此基础上利用不同算法进行滑坡变形位移数值预测。实验显示，粗糙集对滑坡变形阶段划分的准确度达到96．5％，在此基础上利用分类回归树预测滑坡位移的精度达到6．5 mm。结果表明，分阶段的位移预测方法是可行的，其提供的预测精度显著优于普通方法并且达到了工程应用的需求。
위해결대수거량하활파적위이수치정학예측，채용수거알굴기술대활파다원감측수거진행예처리，진이채취조조집이론대수입변량집진행정량평개、약감병완성활파변형계단예측，재차기출상이용불동산법진행활파변형위이수치예측。실험현시，조조집대활파변형계단화분적준학도체도96．5％，재차기출상이용분류회귀수예측활파위이적정도체도6．5 mm。결과표명，분계단적위이예측방법시가행적，기제공적예측정도현저우우보통방법병차체도료공정응용적수구。
Rough set theory is introduced in variables set assessment and reduction for deformation stage prediction in the Baijiabao landslide after its multi-source monitoring data are preprocessed by means of data mining technique. On that basis, serval different algorithms are utilized to predict landslide displacement quantitatively for the purpose of comparison.The tests show that the rough set theory is capable of predicting landslide deformation stage precisely.The results obtained by the rough set contribute to improve performances of numerical prediction of the landslide displacement and the stage-divided method has an advantage over other conventional algorithm.