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Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models

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Abstract

This article emphasizes landslide susceptibility mapping along Ghat road of Kolli hills, Tamil Nadu, India, using frequency ratio, relative effect and fuzzy gamma operator models with the help of remote sensing data and GIS technique. The purpose of the study is to generate, compare and validate landslide susceptibility zones. Landslide inventory was done with data collected from the State Highways department. There are nine landslide-influencing parameters such as slope gradient, slope aspect, slope curvature, relief, lithology, land use and land cover, proximity to road, proximity to drainage, and proximity to lineament, analyzed with help of topo map, existing geology map and satellite data to produce landslide susceptibility maps. Landslide susceptibility maps were generated by calculating relationship between the landslide-influencing factors with past landslide locations using frequency ratio, relative effect and fuzzy gamma operator models. These landslide susceptibility maps were verified and compared using the existing landslide inventory data. The prediction accuracy of frequency ratio model was 87.93 %, for fuzzy gamma operator model was 87.33 %, and for relative effect model it was 85.26 %. Out of which, the frequency ratio model provide maximum prediction accuracy on landslide susceptibility.

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Acknowledgments

The authors acknowledge the Natural Resources Data Management System (NRDMS), Department of Science and Technology, New Delhi, for supporting the project. The authors also thank State Highways and Horticulture departments for providing landslide event and rainfall data.

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  1. Centre for Geoinformatics and Planetary Studies, Department of Geology, Periyar University, Salem, India

    V. Ramesh & S. Anbazhagan

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Ramesh, V., Anbazhagan, S. Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci 73, 8009–8021 (2015). https://doi.org/10.1007/s12665-014-3954-6

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