Citation

Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J. Sci. Total Environ. 2019; 653: 801-814.

Affiliation

University of Natural Resources and Life Sciences (BOKU), Institute of Mountain Risk Engineering, Vienna, Austria.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.scitotenv.2018.10.431

PMID

30759606

Abstract

The Bostanlik district, Uzbekistan, is characterized by mountainous terrain susceptible to landslides. The present study aims at creating a statistically derived landslide susceptibility map - the first of its type for Uzbekistan - for part of the area in order to inform risk management. Statistical index (SI), frequency ratio (FR) and certainty factor (CF) are employed and compared for this purpose. Ten predictor layers are used for the analysis, including geology, soil, land use and land cover, slope, aspect, elevation, distance to lineaments, distance to faults, distance to roads, and distance to streams. 170 landslide polygons are mapped based on GeoEye-1 and Google Earth imagery. 119 (70%) out of them are randomly selected and used for the training of the methods, whereas 51 (30%) are retained for the evaluation of the results. The three landslide susceptibility maps are split into five classes, i.e. very low, low, moderate, high, and very high. The evaluation of the results obtained builds on the area under the success rate and prediction rate curves (AUC). The training accuracies are 82.1%, 74.3% and 74%, while the prediction accuracies are 80%, 70% and 71%, for the SI, FR and CF methods, respectively. The spatial relationships between the landslides and the predictor layers confirmed the results of previous studies conducted in other areas, whereas model performance was slightly higher than in some earlier studies - possibly a benefit of the polygon-based landslide inventory.

Copyright © 2018 Elsevier B.V. All rights reserved.

Language: en

Keywords

Certainty factor; Evaluation; Frequency ratio; Inventory; Landslide; Statistical index