Citation

Gorai AK. J. Indian Soc. Remote Sens. 2022; 50(4): 715-733.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12524-021-01488-2

PMID

unavailable

Abstract

Slope failure in the mining region is one of the frequent accident events that cause damage to both property and life every year. The heavy mechanization and blasting operation in the mines generate a high degree of vibrations, which may cause slope failure. The present study attempts to develop a mechanism for predicting slope failure susceptibility index (SFSI) in mines using remote sensing and GIS technique. SFSI represents the proneness of the landslides of a particular location. The study area selected for the proposed study is the Panchpatmalli bauxite mine, NALCO, located in the Koraput District of Odisha, India. The study also demonstrates the effects of the fuzzification level of various criteria on SFSI through sensitivity analysis. The model validation results indicated that the SFSI in a reported failure zone was higher in the pre-failure condition than the post-failure conditions. Though the sensitivity analysis results showed that the SFSI significantly changed with the level of decision-making attitude and fuzzification level of the individual factors, the relative risks of the zones are more or less uniform. The study results assist in identifying the vulnerable zones, which are highly susceptible to failure. The proposed method can be used in decision-making for effective land-use planning.

Language: en

Keywords

Fuzzy-AHP; Sensitivity analysis; Slope failure susceptibility index; Weighted overlay method