**4. Conclusions**

Coonoor is severely affected by landslides almost every year during periods of intense and prolonged rainfall, causing heavy social and economic losses to its residents. The economy of Coonoor is dependent mainly on tourism and tourism-related activities. Landslide susceptibility mapping can help identify zones that need immediate attention in terms of planning mitigation strategies and development activities. Logistic regression is a more reliable model to map landslide susceptibility compared to other heuristic models like the

analytical hierarchy process model or statistical models like frequency ratio model and is hence used for this study.

The physical and environmental factors causing landslides were identified, and the logistic regression model was used to assess their impact on causing slope instability. The model shows that average annual rainfall, land use, slope morphometry, and soil type are important factors that contribute to slope instability. The landslide susceptibility map indicates the spatial distribution of areas' susceptibility to various degrees of landslide vulnerability. It is a crucial component to ascertain the temporal mapping of landslides. The spatial distribution of susceptibility classes in the region based on the logistic regression model shows that nearly 17.6% of the area is classified as highly unstable i.e., very high susceptible, and 48.6% of total landslides falls under this unstable category i.e., very highly susceptible. Anthropogenic interference is observed to be a very significant factor that has caused landslides in the region as most of the instable areas fall in the densely built-up zones, adjacent to major roads and railway line and in agriculture areas and where forests are disturbed by road infrastructure development like roads and forest plantations.

This study reinforces the need for providing landslide susceptibility maps in hill–town development and planning. It is an indispensable tool for planning land managemen<sup>t</sup> and mitigation strategies. It will also aid the town planners in developing sustainable agriculture practices. It can help in locating regions for future growth in suitable areas of low susceptibility. It can help policymakers in hazard managemen<sup>t</sup> and disaster planning and preparedness at the taluk level. It can also be further used at the block level with the availability of block boundaries. It can also help in drawing policies against land degradation and watershed deterioration.

**Author Contributions:** Conceptualization, E.R.S.; Data curation, E.R.S.; Formal analysis, E.R.S. and V.S.; Funding acquisition, E.R.S.; Investigation, E.R.S.; Methodology, E.R.S. and V.S.; Project administration, E.R.S.; Validation, E.R.S.; Writing—original draft, E.R.S.; Writing—review & editing, V.S. Both authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by NRDMS, DST, gran<sup>t</sup> number DST-NRDMS (155/18-2015) and the APC was funded by Hydrology, MDPI.

**Data Availability Statement:** All the data used in this study will be made available on request to the senior author.

**Acknowledgments:** This study was supported by DST-NRDMS (155/18-2015). The authors would like to acknowledge the financial support rendered by NRDMS, DST, for the research with thanks. We also would like to thank the Vice-Chancellor of SASTRA Deemed University, Thanjavur, India, for the support and the facilities to carry out this work. We acknowledge the partial support received by the corresponding author from the Virginia Agricultural Experiment Station (Blacksburg) and the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture (Washington, D.C.).

**Conflicts of Interest:** The authors declare no conflict of interest.
