Varya Bazilova has published the first paper of her PhD in a collaboration between Mountain Hydrology and dr. Tjalling de Haas.
In the study, she used machine learning to assess debris flow and flood hazards on 1,793 alluvial fans across High Mountain Asia. The results highlight how catchment shape, especially slope and size, plays a key role in determining whether a fan is more prone to floods or debris flows. It also shows that while climate factors have a more limited influence, it should not be neglected. This study helps improve our understanding of mountain hazards and shows the potential of machine learning in geomorphology.
Read the full (open access) paper here.
