An artificial intelligence based framework to analyze the landside risk of a mountainous highway

Abstract

The present study entails an artificial intelligence-based framework for landslide risk analysis of a highway infrastructure in the Himalayan region. In total, 241 landslide polygons that were inventoried for the study area. The spatial component of landslide susceptibility map was prepared by incorporating drainage density, TWI, geology, elevation and slope gradient as major contributing factors, in the certainty factor–random forest (CF-RF) hybrid model with accuracy of 0.928. The landslide hazard analysis was carried out by multiplying landslide spatial and temporal probabilities. The landslide vulnerability analysis of the highway stretch was carried out by integrating the elements at risk. The built-up area was extracted by using U-Net deep learning algorithm with an accuracy of 0.964. The landslide risk map of the highway stretch prepared by the multiplication of landslide hazard and vulnerability maps depicts that 16.78% and 6.25% of the study area falls in high and very high-risk zones, respectively.

Keywords

• landslide risk
• machine learning
• deep learning
• artificial intelligence
• remote sensing