Abstract
Runoff predictions of a river from meteorological inputs is a key task in the field of hydrology. However, current hydrological models require a substantial amount of parameter tuning on basis of historical records. If no historical runoff observations are available it is very challenging to produce good predictions. In this study we explore the capability of LSTMs for simulating the runoff for these ungauged cases. A single LSTM, also including static catchment attributes as input, is trained to learn a general hydrological model from hundreds of catchments throughout the contiguous United States of America and evaluated against catchments not used during training. Our results suggest that LSTMs a) are able to learn a general hydrological model and b) in the majority of catchments outperform an established hydrological model, which was especially trained for these catchments.
| Original language | English |
|---|---|
| Title of host publication | Neural Information Processing Systems (NIPS 2018) |
| Number of pages | 1 |
| Publication status | Published - 2018 |
Fields of science
- 303 Health Sciences
- 304 Medical Biotechnology
- 304003 Genetic engineering
- 305 Other Human Medicine, Health Sciences
- 101004 Biomathematics
- 101018 Statistics
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102004 Bioinformatics
- 102010 Database systems
- 102015 Information systems
- 102019 Machine learning
- 106023 Molecular biology
- 106002 Biochemistry
- 106005 Bioinformatics
- 106007 Biostatistics
- 106041 Structural biology
- 301 Medical-Theoretical Sciences, Pharmacy
- 302 Clinical Medicine
JKU Focus areas
- Computation in Informatics and Mathematics
- Nano-, Bio- and Polymer-Systems: From Structure to Function
- Medical Sciences (in general)
- Health System Research
- Clinical Research on Aging