Spotlight talk at the AI for Earth Sciences workshop of the NeurIPS 2020, presenting introduction of the world and terminology of hydrology/streamflow prediction for data scientists.
Abstract
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.
Paper
Video of the presentation: https://slideslive.com/38941247/
Citation
@inproceedings{gauch2020neurips,
title={A Machine Learner's Guide to Streamflow Prediction},
author={Gauch, M. and Klotz, D. and Kratzert, F. and Nearing, S. and Hochreiter, S. and Lin, J.},
booktitle={Workshop on AI for Earth Sciences 34th Conference on Neural Information Process-ing Systems (NeurIPS 2020)},
venue={Vancouver, Canada},
date={6-12 Dec},
year={2020}
}