CLIMIS4AVAL

Motivation

‍The IMIS network is widely used in many applications ranging from natural hazards to ecology. However, widespread and systematic use of data measured by IMIS stations is hindered by the many measurement anomalies and sensor outliers. In CLIMIS4AVAL we aim at performing application-agnostic anomaly and outlier detection, data imputation and downscaling and producing a maintenance model predicting whether a given measurement sensor is working as expected or not. In particular, the project focuses on the measurements of snow depth, wind speed and direction, air temperature, precipitation, and it will be validated by use of this data in routine application within WSL.

Proposed Approach / Solution

‍The SDSC is working with WSL and SLF to develop statistical and machine learning methods for anomaly detection in time series, outlier detection, and imputation for given IMIS parameters. We work on both traditional statistical methods and advanced deep learning forecasting methods.  

As an example, we developed a machine learning tool to support parsing and exploitation of snow height measurements from Automated Weather Stations (AWS) in the IMIS network. Some stations rely on an ultrasonic or laser sensor to measure the height above the soil of material accumulating under the station. In winter season, in standard conditions, this is clearly related to snow height, while in summertime to vegetation growth. However, crucial measurements of snow height occur in spring and autumn, where due to ambiguous and fast changing environmental conditions it is very hard to assign the measurement to either snow or other factors using only, for instance, temperature thresholds. To this end, we trained a deep learning classifier taking as input multivariate environmental parameters to predict whether the station observes snow or not. It is currently used internally at SLF to flag measurements that are likely related to snow and can therefore be used in downstream tasks. Figure 1 summarizes the pipeline; more details can be found in  the work of Svoboda et al. (2024).

Impact

‍Numerical avalanche prediction and other models used at SLF will be more accurate with the cleaned data. The cleaned data will also be made openly available in the data portal of SLF, and will therefore benefit the numerous downstream users of the data, ranging from hydrological modelers, avalanche forecasters, and alpine ecologists.