Floods are shaped by complex and nonlinear interactions between weather patterns, rainfall, soil properties, topography, land cover, and how wet a catchment already is. This complexity is one reason machine learning (ML) is increasingly used in hydrology. ML models can learn patterns from large and complex datasets with many variables. Early in my PhD, while exploring machine learning models for ...[Read More]
What machine learning can and cannot tell us about floods
