- GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel
Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification or timeseries prediction). However, these models tend to produce black-box results and are often difficult to interpret. Here we present Echo State Networks (ESNs) as a certain type of recurrent ANNs, also known as reservoir computing. ESNs are easy to train and only require a small number of trainable parameters. They can be used not only for timeseries prediction but also for image classification, as shown here: Our ESN model serves as a detector for El Nino Southern Oscillation (ENSO) from sea-surface temperature anomalies. ENSO is actually a well-known problem and has been widely discussed before. But here we use this simple problem to open the black-box and apply layerwise relevance propagation to Echo State Networks.