Landslide Hazard Model

Modelling of Landslide Hazard Modeling and its risk assessment using Remote Sensing and GIS techniques

Abstract

This research examines the use of GIS and Remote Sensing techniques to model landslide hazard and its risk assessment in and around Wau Bulolo. In achieving this, risk evaluation is carried out with the use of eleven geotechnical engineering parameters which include altitude, slope, aspect, rainfall, lithology, soil erodibility function, landform, river buffer, road buffer, earthquake magnitude, and land use/ land cover to generate and calculate landslide susceptibility zones and risk zones. Landslide vulnerability of Wau/ Bulolo District in Morobe Province was assessed using the Weighted Linear Combination (WLC) method. The study

The aim of this research is to model the landslide hazards and generate a landslide susceptible map by carrying out the risk assessment. Use of GIS in this study is to map out potentially hazardous new landslide areas, mudflows and developing landslide help to promote advance awareness of risks at hand and notify authorities (Oaego and Kuna 2006) with the tool to mitigate or engineer out the problem. Remote sensing allows for regional and extensive data collection at any given time saving cost and time with comparative analysis over time (Samanta et al. 2011 and

2011) to generate landslide susceptibility map. Eleven parameters selected purposely for this project are; slope angle, altitude, rainfall, aspect, geological unit, erodibility function, landform, river buffer, road buffer, land use/ land cover and recent earthquake magnitude. There are three main parts to the methodology and they are (i) Deriving and generating parameters from the datasets, (ii) ranking, classifying and weighing of the parameters and (iii) generating of Landslide hazard and risk factor map using the weighted sum tool in ArcGIS 10. Slope, aspect, altitude were derived from digital elevation model (DEM) of Bulolo and land use/land cover from the Landsat 5 TM (30m) data sets of year 1992. Proximity analysis (buffer) was performed (Michael and Samanta 2016) to generate zones of vulnerability from the rivers and the roads. The overall methodological flow chart is shown in figure