TY - JOUR
T1 - Application of bivariate statistical techniques for landslide susceptibility mapping
T2 - A case study in Kaghan Valley, NW Pakistan
AU - Tanoli, Javed Iqbal
AU - Jehangir, Adeel
AU - Qasim, Muhammad
AU - Rehman, Mohib ur
AU - Shah, Syed Tallataf Hussain
AU - Ali, Muhammad
AU - Jadoon, Ishtiaq Ahmad Khan
N1 - Publisher Copyright:
© 2023 John Wiley & Sons Ltd.
PY - 2023/12
Y1 - 2023/12
N2 - Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.
AB - Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.
KW - Information Value Method
KW - Kaghan valley
KW - Landslide Numerical Risk Factor
KW - North Pakistan
KW - Statistical Index
KW - bivariate statistics
KW - landslide susceptibility
UR - http://www.scopus.com/inward/record.url?scp=85165156059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165156059&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/3b9c1dcf-0672-38f4-921e-72f40f874a45/
U2 - 10.1002/gj.4836
DO - 10.1002/gj.4836
M3 - Article
AN - SCOPUS:85165156059
SN - 0072-1050
VL - 58
SP - 4576
EP - 4595
JO - Geological Journal
JF - Geological Journal
IS - 12
ER -