TY - JOUR
T1 - Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logic regression models
AU - Das, Iswar Das
AU - Stein, A.
AU - Kerle, N.
AU - Dadhwal, Vinay K.
PY - 2012
Y1 - 2012
N2 - Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular.
AB - Landslide susceptibility mapping (LSM) along road corridors in the Indian Himalayas is an essential exercise that helps planners and decision makers in determining the severity of probable slope failure areas. Logistic regression is commonly applied for this purpose, as it is a robust and straightforward technique that is relatively easy to handle. Ordinary logistic regression as a data-driven technique, however, does not allow inclusion of prior information. This study presents Bayesian logistic regression (BLR) for landslide susceptibility assessment along road corridors. The methodology is tested in a landslide-prone area in the Bhagirathi river valley in the Indian Himalayas. Parameter estimates from BLR are compared with those obtained from ordinary logistic regression. By means of iterative Markov Chain Monte Carlo simulation, BLR provides a rich set of results on parameter estimation. We assessed model performance by the receiver operator characteristics curve analysis, and validated the model using 50% of the landslide cells kept apart for testing and validation. The study concludes that BLR performs better in posterior parameter estimation in general and the uncertainty estimation in particular.
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=http://dx.doi.org/10.1016/j.geomorph.2012.08.004
UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2012/isi/kerle_lan.pdf
U2 - 10.1016/j.geomorph.2012.08.004
DO - 10.1016/j.geomorph.2012.08.004
M3 - Article
SN - 0169-555X
VL - 179
SP - 116
EP - 125
JO - Geomorphology
JF - Geomorphology
ER -