Spatial Prediction of Landslide susceptibility zones using Artificial Neural Network in the Sikkim Himalaya, India

Abstract

<p>Landslide is one of the most destructive natural hazard in Himalaya. It is mainly caused by numerous geological, geomorphological and hydrological characteristics of the terrain, and generally triggered either by rainfall or earthquake. It poses a serious threat to human lives, environment and the built infrastructures of the region. It has been reported that every year around 300 - 400 fatalities occur in the Himalayan region and monetary loss incurred is ~ 100 million USD. Therefore it is necessary to demarcate different landslide susceptible zones in the region. This will help in the sustainable development of region and minimize the destruction caused by landslides. For the present study, large scale landslide susceptibility mapping for the state of Sikkim encompassing northern and eastern districts using Artificial Neural Network has been carried out.</p><p> </p><p>Landslide susceptibility, the relative probability of occurrence of landslides in an area, is one of the prerequisites for the development of the area in this mountain terrain. To assess the landslide susceptibility in a region, it is essential to understand the spatial distribution of the active landslides and landslide deposits, and their controlling factors. The relative weightage to each landslide controlling factor is determined using appropriate models and finally the landslide susceptibility map is prepared.</p><p>Geologically, the area encompasses the rocks of the Lesser Himalaya and Higher Himalaya, demarcated from one another by Main Central Thrust (MCT) and mainly constitutes phyllite, schist, quartzite, schist and gneiss. An inventory of 247 active landslides and landslide deposits ranging in area from ~ 200 m<sup>2</sup> to ~ 450700 m<sup>2</sup> and thematic layers of fifteen possible causative factors of landslides viz. lithology, slope angle & aspect, elevation, curvature-plan, curvature-profile, topographic wetness index, stream power index, distance to drainage, road & thrusts, land use and land cover, normalized difference vegetation index (NDVI), and peak ground acceleration (PGA) have been prepared. Of the 247 landslides, 70% were randomly selected for the assessment of landslide susceptibility, and the remaining 30% were used for validating the model. The dependency rate of landslides on each causative factor were estimated using information gain value analysis and subsequently landslide susceptibility map was computed using artificial neural network (ANN) algorithm.</p><p>It has been noted that high and very high susceptible zones are mainly concentrated along the strike of the MCT, on south facing slopes as these are slopes experience concentrated rainfall due to the orographic barrier. The success rate of our model is 92% and prediction rate is 89%.</p>