LANDSLIDEV: A VISUAL ANALITICS SYSTEM FOR MODEL COMPARISON IN LANDSLIDE PREDICTION

MPhil Thesis Defence


Title: "LANDSLIDEV: A VISUAL ANALITICS SYSTEM FOR MODEL COMPARISON IN 
LANDSLIDE PREDICTION"

By

Miss Yifan MU


Abstract

Landslide and debris flows are hazards that can result in huge victims and 
economic losses. Landslide prediction is one of the most important 
approaches to mitigate these hazards’ effects, in which selecting suitable 
models for prediction is crucial. However, current model selection heavily 
depends on traditional evaluation metrics of the entire dataset, which are 
ineffective and inefficient due to the data complexity in the temporal and 
spatial analysis, and prediction priority among periods and locations. 
This thesis proposes LandslideV, a visual analytics system to 
interactively facilitate the model comparison for landslide prediction. It 
provides a comprehensive comparison among models using different features 
in various periods and regions. Besides, it supports further model 
optimization based on oversampling subsets of data of poor performance or 
high importance. Case studies with the real-world dataset are conducted to 
evaluate the effectiveness and applicability of LandslideV.


Date:  			Thursday, 28 July 2022

Time:			10:00am - 12:00noon

Zoom Meeting:
https://hkust.zoom.us/j/98361711889?pwd=dE9wUWFjckFOVURYVUpzY3Z0NU1NQT09

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Prof. Charles Ng (Supervisor)
 			Prof. Ke Yi (Chairperson)
 			Dr. Dan Xu


**** ALL are Welcome ****