A Machine Learner’s Guide to Streamflow Prediction
Martin Gauch, Daniel Klotz, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and and Jimmy Lin
Although often subconsciously, many people deal with water-related issues on a daily basis. For instance, many regions rely on hydropower plants to produce their electricity, and, at the extreme, floods and droughts pose one of the big environmental threats of climate change. At the same time, many machine learning researchers have started to look beyond their field and wish to contribute to environmental issues of our time. The modeling of streamflow—the amount of water that flows through a river cross-section at a given time—is a natural starting point to such contributions: it encompasses a variety of tasks that will be familiar to machine learning researchers, but it is also a vital component of flood and drought prediction (among other applications). Moreover, researchers can draw upon large open datasets, sensory networks, and remote sensing data to train their models. As a getting-started resource, this guide provides a brief introduction to streamflow modeling for machine learning researchers and highlights a number of possible research directions where machine learning could advance the domain.
NeurIPS Workshop: AI for Earth Sciences, 2020-12-12.