In this online workshop and live practice, you can learn the complete process (A-Z) from scratch to production.  How to select parameters and why, download raster and vector data, processing data, images in ArcGIS environment, justification of variables, step by step guide of Dempster-Shafer theory of EBF and FR models, produced prediction map applying combined EBF and FR methods in Landslide prediction zonation using ArcGIS. Moreover, you will also learn validation of the susceptibility map using advanced techniques such as success rate curve and prediction rate curve.

Evidence Belief Function (EBF)

The Dempster-Shafer theory is a mathematical-based model with a bivariate statistical methodology. This theory is known as the theory of belief functions. The Dempster-Shafer theory has been used in landslide susceptibility mapping by several. There are four basic evidential belief functions used: Bel (degree of belief), Dis (degree of disbelief), Unc (degree of uncertainty) and Pls (degree of plausibility). Bel and Pls indicate the lower and upper bounds of the probability; Dis is the belief of the proposition being false on given evidence; Unc means the difference between the belief and the plausibility. During this e-workshop, you can learn how to prepare these four maps and their relevance in landslide prediction.

Frequency ratio (FR)

Frequency ratio (FR) is one of the statistical methods frequently used and adopted for the present study to produce LSZ map using multi−class spatial data sets. This method is a simple and understandable probabilistic method. FR is defined as the ratio of the area where landslides occurred to the total study area. It is the ratio of the probabilities of a landslide occurrence to a non−occurrence for a given landslide conditioning sub−class. The FR of each factor's sub−classes are calculated from their relationship with landslide events. During this workshop the landslide susceptibility index for the FR model will be calculated by the addition of FR values for each sub−class's landslide conditioning factors.

After completing this course, you will be efficiently able to process, predict, and validate any data related to hazard, vulnerability, risk, and suitability assessment using the EBF and FR models.