Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-09, Vol.12 (17), p.2757
• Main Authors: Nachappa, Thimmaiah, Ghorbanzadeh, Omid, Gholamnia, Khalil, Blaschke, Thomas
• Format: Article
• Language: English
• Subjects: Deep learning; Earthquakes; Elevation; Exposure; exposure mapping; flood; Flood management; Flood mapping; Flooding; Floods; Forest & brush fires; Geology; Geomorphology; Hazards; Hydrologic data; Infrastructure; Land cover; landslide; Landslides; Landslides & mudslides; Learning algorithms; Lithology; Machine learning; multi-hazard; Neural networks; Normalized difference vegetative index; Rain; Rainfall; random forest (RF); support vector machine (SVM); Support vector machines; Training
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12172757

We live in a sphere that has unpredictable and multifaceted landscapes that make the risk arising from several incidences that are omnipresent. Floods and landslides are widespread and recurring hazards occurring at an alarming rate in recent years. The importance of this study is to produce multi-hazard exposure maps for flooding and landslides for the federal State of Salzburg, Austria, using the selected machine learning (ML) approach of support vector machine (SVM) and random forest (RF). Multi-hazard exposure maps were established on thirteen influencing factors for flood and landslides such as elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), geology, lithology, rainfall, land cover, distance to roads, distance to faults, and distance to drainage. We classified the inventory data for flood and landslide into training and validation with the widely used splitting ratio, where 70% of the locations are used for training, and 30% are used for validation. The accuracy assessment of the exposure maps was derived through ROC (receiver operating curve) and R-Index (relative density). RF yielded better results for both flood and landslide exposure with 0.87 for flood and 0.90 for landslides compared to 0.87 for flood and 0.89 for landslides using SVM. However, the multi-hazard exposure map for the State of Salzburg derived through RF and SVM provides the planners and managers to plan better for risk regions affected by both floods and landslides.