Abstract
A deep learning rainfall-runoff model can take multiple meteorological forcing products as inputs and learn to combine them in spatially and temporally dynamic ways. This is demonstrated using Long Short Term Memory networks (LSTMs) trained over basins in the continental US using the CAMELS data set. Using multiple precipitation products (NLDAS, Maurer, DayMet) in a single LSTM significantly improved simulation accuracy relative to using only individual precipitation products. A sensitivity analysis showed that the LSTM learned to utilize different precipitation products in different ways in different basins and for simulating different parts of the hydrograph in individual basins.
| Original language | English |
|---|---|
| Number of pages | 26 |
| Journal | Hydrology and Earth System Sciences Discussions |
| DOIs | |
| Publication status | Published - 2021 |
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