Open AccessSpatial prediction of landslide susceptibility based on the neighborhood rough set
Author(s) -
Xin Yang,
Rui Liu,
Luyao Li,
Mei Yang,
Yuantao Yang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/780/7/072052
Subject(s) - landslide , granularity , rough set , support vector machine , random forest , data mining , data set , set (abstract data type) , computer science , artificial intelligence , geology , geotechnical engineering , programming language , operating system
This paper discusses the feasibility of reducing the landslide inducing factors by the neighborhood rough set algorithm in data processing section, which could improve the accuracy and timeliness of landslides susceptibility prediction models effectively. 15 predisposing factors for a continuous value that has not been graded were reduced by nighborhood rough set, a granularity calculation method, based on the importance degree of each factor. Then the combination of factors before and after optimization was put into random forest (RF) and support vector machine (SVM) for modelling. ROC curve and statistical indicators show that: the average performance of the reduced factors combination is superior to that before optimization. In addition, we used the RF which has a better performs in evaluation to map the landslides susceptibility in Jiuzhaigou area, discuss the timeliness of the assessment of landslides prediction and the weight of the predisposing factors.