This document summarizes a presentation on a project mapping landslide susceptibility in the Nandanagar region of Chamoli district, India using a frequency ratio approach. The presentation was prepared by Santosh for their MSc program under the supervision of Dr. Arvind Bhatt. It introduces the study area and motivation, describes the methodology using nine conditioning factors to calculate frequency and prediction rates, and presents the results of the landslide susceptibility map and its 87.2% accuracy validated using AUC. Limitations include the resolution of the DEM data and scale of land use/geology data.

1. A presentation
on
project
“Landslide Susceptibility Mapping using Frequency
Ratio approach in Nandanagar region of Chamoli
district”
Prepared By-
Santosh
M.Sc. IVth Semester
Dept. of Geology
Govt. P. G. College
Gopeshwar
Project Supervised by-
Dr. Arvind Bhatt
Head, Dept of Geology
Govt. P. G. College
Gopeshwar

2. Introduction Study Area Methodology Results Conclusion
• A landslide is defined as the movement of a mass of
rock, debris, or earth down a slope.
• Landslide susceptibility can be defined as possibility of
occurrence of a landslide in an area.
• Landslide Susceptibility depends on various geo-
environmental factors. These are known as landslide
conditioning factors.

3. Introduction Study Area Methodology Results Conclusion
MOTIVATION OF THE STUDY
• Landslide is one of most critical natural disasters that cause a
significant loss to nature and mankind.
• Himalayan region itself has contributed ~ 76% to the landslide
susceptible area of the Indian landmass and causes the loss of
human lives of ~ 200 with the economic loss of ~ 550 crores in
every year
• Study area face a large number of Landslide and Flash Flood
activity every year during monsoons. Most of the landslides in
the area are caused by heavy rainfall/cloudburst.

4. Introduction Study Area Methodology Results Conclusion

5. Introduction Study Area Methodology Results Conclusion
• Study area lies in inner lesser
Himalayas of Chamoli district of
Uttarakhand, and comprise of some
part of The Nandprayag Klippe.
• The area is most prone to frequent
landslides, given the rugged
topography, active seismicity,
fractured geology, climatic factors and
anthropogenic activities on unstable
slopes.

6. Introduction Study Area Methodology Results Conclusion
• Study area falls in Lesser Himalayan sequence, and mainly comprise of
high hills, V-shaped fluvial valley, river terraces and Denudational
Structural Mountain.
• Phyllites, Schists, Augen Gneiss, Quartzite and amphibolite are the main
rock types that comprises the study area.
• The study area is demarcated by one major active thrust plane(Baijnath
Thrust) and several Tear faults(e.g. Nandprayag Fault).
• The Baijnath Thrust in Eastern margin of study area separates
Nandprayag klippe from underlying Chamoli Quartzite unit.

7. Introduction Study Area Methodology Results Conclusion
• In present study the Frequency Ratio technique is used to map
landslide susceptible zones.
• The main principle of this method is based on assessment of observed
spatial relationship between past landslides and a set of landslide
conditioning factors.
• Frequency Ratio (FR) is defined as ratio of percentage of landslide
pixels in a class and total percentage of pixels in same class.
• Higher FR value indicates stronger observed spatial relationship
between the landslide occurrence and landslide conditioning factor

8. Introduction Study Area Methodology Results Conclusion
• Nine Conditioning Factors are used
in present study to asses landslide
susceptibility.
1. Lithology
2. Distance to Faults
3. Slope Gradient
4. Slope Aspect
5. Elevation
6. Plane Curvature
7. Land use and Landcover
8. Distance to Drainage
9. Distance to Roads
Geological
Factors
Topographic
Factors
Environmental
Factors
Anthropogenic
• FR value is changed into Normalised Relative
Frequency value in present study for better
comparison and understanding the influence in
computation of susceptibility
• Relative Factor RF is ratio of Frequency ratio
of an individual class and sum of Frequency
ratio of each class of an individual Factor.
• In order to normalise RF values, they are
multiplied by 100 and then nearest integer
value is taken as Normalised Relative
Frequency (NRF).
• Prediction Rate PR (weight) of each
Conditioning factor is calculated using formula

9. Introduction Study Area Methodology Results Conclusion
Landslides
Classified
Factor maps Cross Table Reclassified Map
Geology
Slope
Maps
Overlay
+
+
x Weight Value
x Weight Value
x Weight Value
Landslide Susceptibility Map
Validation
• The model is validated using
Area under Curve(AUC)
• Higher AUC value depicts High
accuracy of model
• The curve is prepared by
plotting false positive rate in x-
axis against true positive rate in
y- axis.
• True positive = the number of
observed landslides predicted
accurately.
• False positive = number of
occurring landslides
inaccurately categorized in the
non- landslide classes
Generation of Landslide Susceptibility Map

10. Introduction Study Area Methodology Results Conclusion

11. Introduction Study Area Methodology Results Conclusion

12. Introduction Study Area Methodology Results Conclusion
Conditioning Factor Weightage
Validation Using AUC(Area Under Curve)
Success Rate Prediction Rate
• AUC= 87.2%
• Success rate AUC reflects how
well the model matches the
events that had already been
used in training the model.
• AUC= 75.6%
• Prediction rate AUC shows
how well a model can
predict future event

13. Introduction Study Area Results Conclusion
Methodology
• Slope, land cover, lithology, are the most important natural factors affecting
landslides.
• LSM showing Very High Susceptibility in inhabited areas at several
locations(Villages-Narangi, Sironsar, Mathkot, Phali, Dhar, Sainti) in the area.
• The outcomes of this research can be used in predicting future landslides.
Similarly, it can be helpful in planning for infrastructure development and
resettlement activities.

14. Introduction Study Area Results
Methodology
LIMITATION OF THE STUDY
The limitations of this research work are as follows: -
• This research was analysed based on 30m resolution Cartosat-1 CartoDEM. from which landslide
conditioning factors such as slope, aspect, curvature and elevation were generated. Hence, the overall
results were based on DEM resolution which may affect accuracy.
• In practice land use, land cover and geology are more contributing factor for landslide occurrence but
the available data are of small scale and variation is only observed in large spatial extent which affects
overall accuracy of model.
• The landslide inventory may not be mapped properly in higher elevation zone.
Conclusion

15. Questions, suggestions and comments are
always welcome!