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Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis
Author(s) -
Gorsevski Pece V,
Gessler Paul E,
Foltz Randy B,
Elliot William J
Publication year - 2006
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/j.1467-9671.2006.01004.x
Subject(s) - landslide , logistic regression , hazard , terrain , geographic information system , probabilistic logic , spatial analysis , receiver operating characteristic , cartography , remote sensing , geography , environmental science , computer science , geology , artificial intelligence , machine learning , geomorphology , ecology , biology
An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.