Bilal Aslam, Ahsen Maqsoom, Umer Khalil, Omid Ghorbanzadeh, Thomas Blaschke, Danish Farooq, Rana Faisal Tufail, Salman Ali Suhail, and Pedram Ghamisi

Flow chart

Methodology flow chart.

This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM.

Sensors, 22, 9, 3107, 2022-04-19.

View paper
IARAI Authors
Omid Ghorbanzadeh, Dr. Pedram Ghamisi
Earth Observation
Change Detection, Landslide Detection, Linear Regression, Logistic Regression, Machine Learning, Support Vector Machines


Imprint | Privacy Policy

Stay in the know with developments at IARAI

We can let you know if there’s any

updates from the Institute.
You can later also tailor your news feed to specific research areas or keywords (Privacy)

Log in with your credentials

Forgot your details?

Create Account