Impact of EV Charging Stations on Power System Quality

Abstract

Landslides are among the most destructive natural disasters that may occur in hilly terrain such as the Himalaya. The study of landslides has gotten a lot of interest lately, mostly because people are becoming more conscious of the socio-economic consequences of landslides. Remote sensing pictures give a wealth of important land use information that may be combined in a GIS setting with other spatial characteristics that influence the incidence of landslides to get a more complete picture of the landscape. The creation of a landslide inventory is an essential step in conducting a landslide hazard analysis using geographic information systems (GIS)[1].

The use of geographic information systems (GIS) enabled the rapid analysis of a large amount of data, and the artificial neural network proved to be an excellent tool for landslide hazard estimates. In order to perform a risk analysis, the DEM, the distance from the danger zone, the land cover map, and the damageable items that were at risk were all considered. Demarcating catchments and masking risky zones in the landslide area were accomplished via the use of digital elevation models (DEMs). The hazard map was generated via the use of geographic information system (GIS) map overlaying technology. This information might be used to calculate the danger to people, property, and existing infrastructure, such as transportation.

As part of the effort to develop real-time weather forecasting and image processing methodologies, this study may benefit from the addition of concepts and technologies such as embedded systems, the Internet of Things, and digital image processing to its repertoire.