Abstract
Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrological forecasts require models that are calibrated to past data.
In this work we present a large-scale LSTM-based modeling approach that - by training on large data sets - learns a diversity of hydrological behaviors. Previous work shows that this model is more accurate than current state-of-the-art models, even when the LSTM-based approach operates out-of-sample and the latter in-sample. In this work, we show how this model can assess the sensitivity of the underlying systems with regard to extreme (high and low) flows in individual watersheds over the continental US.
| Original language | English |
|---|---|
| Title of host publication | Neural Information Processing Systems Foundation (NeurIPS 2019) |
| Number of pages | 1 |
| Publication status | Published - 2019 |
Fields of science
- 305907 Medical statistics
- 202017 Embedded systems
- 202036 Sensor systems
- 101004 Biomathematics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101031 Approximation theory
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 102018 Artificial neural networks
- 102019 Machine learning
- 102032 Computational intelligence
- 102033 Data mining
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 202035 Robotics
- 202037 Signal processing
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
JKU Focus areas
- Digital Transformation
