ML for Flood Forecasting at Scale

Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. Machine learning (ML) is primed to be useful in this scenario: learned models often surpass human experts in complex high-dimensional scenarios, and the framework of transfer or multitask learning is an appealing solution for leveraging local signals to achieve improved global performance. We propose to build on these strengths and develop ML systems for timely and accurate riverine flood prediction. Floods are the most common and deadly natural disaster in the world. Every year, floods cause from thousands to tens of thousands of fatalities [1, 22, 2, 21, 14], affect hundreds of millions of people [14, 21, 2], and cause tens of billions of dollars worth of damages [1, 2]. These numbers have only been increasing in recent decades [23]. Indeed, the UN charter notes floods to be one of the key motivators for formulating the sustainable development goals (SDGs), and directly challenges us: "They knew that earthquakes and floods were inevitable, but that the high death tolls were not."