Landslide susceptibility mapping in the downstream region of Sharavathi river basin, Central Western Ghats

Landslides are hazards encountered during monsoon in undulating terrains of Western Ghats causing geomorphic make over of earth surface resulting in significant damages to life and property. An attempt is made in this paper to identify landslides susceptibility regions in the Sharavathi river basin downstream using frequency ratio method based on the field investigations during July- November 2007. In this regard, base layers of spatial data such as topography, land cover, geology and soil were considered. This is supplemented with the field investigations of landslides. Factors that influence landslide were extracted from the spatial database. The probabilistic model -frequency ratio is computed based on these factors. Landslide susceptibility indices were computed and grouped into five classes. Validation of LHS, showed an accuracy of 89% as 25 of the 28 regions tallied with the field condition of highly vulnerable landslide regions. The landslide susceptible map generated for the downstream would be useful for the district officials to implement appropriate mitigation measures to reduce hazards.

Landslide Susceptible Locations in Western Ghats: Prediction through openModeller

Many shallow landslides are triggered by heavy rainfall on hill slopes resulting in enormous casualties and huge economic losses in mountainous regions. Hill slope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed due to a number of reasons such as increased soil moisture content, change in land use causing slope instability, etc. Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration and information related to land surface susceptibility. Terrain analysis applications using spatial data such as aspect, slope, flow direction, compound topographic index, etc. along with information derived from remotely sensed data such as land cover / land use maps permit us to quantify and characterise the physical processes governing the landslide occurrence phenomenon. In this work, the probable landslide prone areas are predicted using two different algorithms – GARP (Genetic Algorithm for Rule-set Prediction) and Support Vector Machine (SVM) in a free and open source software package - openModeller. Several environmental layers such as aspect, digital elevation data, flow accumulation, flow direction, slope, land cover, compound topographic index, and precipitation data were used in modelling. A comparison of the simulated outputs, validated by overlaying the actual landslide occurrence points showed 92% accuracy with GARP and 96% accuracy with SVM in predicting landslide prone areas considering precipitation in the wettest month whereas 91% and 94% accuracy were obtained from GARP and SVM considering precipitation in the wettest quarter of the year.