Landslide risk analysis using artificial neural network model focussing on different training sites

This paper presents landslide hazard and risk analysis using remote sensing data, GIS tools and artificial neural network model. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. These factors were used with artificial neural network to analyze landslide hazard. Each factor’s weight was determined by the back-propagation training method. Then the landslide hazard indices were calculated using the trained back-propagation weights, and the landslide hazard map was created using GIS tools. Landslide locations were used to verify results of the landslide hazard maps and to compare them. The results of the analysis were verified using the landslide location data and compared with neural network model with all cases. The accuracy observed was 83, 72, 82, 79 and 81% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. Further, risk analysis has been performed using DEM, distance from hazard zone, land cover map and damageable objects at risk. DEM was used to delineate the catchments and served as a mask to extract the highest hazard zones of the landslide area. Risk map was produced using map overlying techniques in GIS. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

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