Predicting Wildfire Susceptibility using GeoAI Approaches

 Suggested by: Omid Reza Abbasi, Johannes Scholz 

Keywords: Wildfire Prediction, GeoAI, Machine Learning, Remote Sensing, GIS, Environmental Modeling, Wildfire Susceptibility, Spatial Analysis, Risk Assessment 

 

Objective: Develop a predictive model for wildfire susceptibility in Austria using GeoAI approaches. The model will analyze geospatial and environmental variables—such as vegetation index, temperature, and human activity patterns—to assess and predict wildfire risk areas. 

 

Short Description: This thesis explores the application of GeoAI techniques for predicting wildfire susceptibility in Austria. By leveraging machine learning models and geospatial datasets, the study aims to identify high-risk areas based on environmental and anthropogenic factors. The research will integrate GIS-based spatial analysis with predictive modeling to enhance early warning systems and wildfire management strategies. 

 

Start: 

As soon as possible 

 

Prerequisites/qualification: 

• Knowledge in GIS and spatial data processing 

• Interest/Knowledge in Machine Learning 

• Interest in Causality