D05
Deep generative networks for detecting anomalous events in the water cycle
Prof. Dr. Juergen Gall
University of Bonn | +49 228 73-69600 |
PD Dr. Petra Friederichs
University of Bonn | +49 228 73-5187 |
Summary
Although there is a general expectation that extreme events in the water cycle are occurring more frequently and become stronger due to climate change, it remains a challenge to identify them in large simulation data sets. While extreme events can be defined based on impact indicators like agricultural droughts, these indicators do not cover all extreme events. We therefore aim to identify extreme events in simulated water cycle components by developing novel deep networks that detect anomalous events in simulated data.
Graphical summary
Fig. 1: Using data from simulations, the deep network detects anomalous events.
Contribution to the CRC
D05 addresses the central hypothesis of CRC by developing machine learning techniques to analyze simulation data focusing on detecting anomalous patterns in simulated variables.
Approach
For predicting extreme events such as agricultural droughts or wildfire, we will train deep neural network on TerrSysMP data as well as remote sensing data. The deep networks will be first trained using full supervision, i.e., they will be trained by taking the generated data as input and minimizing the difference between the predicted and annotated extremes (e.g., wildfire or droughts). In a second step, we aim to train the networks unsupervised in order to identify anomalous events in simulated data.
Main Results in 2022
In collaboration with B03, we developed a deep learning approach that predicts when weather conditions have a high tendency to cause an extreme event such as large wildfire events. Shams Eddin M. H., Roscher R., Gall J., “Location-aware Adaptive Denormalization: A Deep Learning Approach for Wildfire Danger Forecasting”. arXiv preprint arXiv:1608.05971. https://doi.org/10.48550/arXiv.2212.08208, 2022.
Fig. 2: We propose a convolutional neural network for wildfire danger forecasting that handles static and dynamic variables differently. Since the static variables do not change over time, they are processed by a branch consisting of 2D convolutions while the dynamic variables are processed by the second branch with 3D convolutions. To address the causal effect of static variables on dynamic variables, we introduce feature modulation for the dynamic variables (LOADE) where the modulation parameters are generated dynamically and conditionally on the geographical location.
Fig. 3: Qualitative results produced by the proposed approach. The black circles represent an ignition of a large wildfire on that day.
