Lessons learned from deformation nowcasting at a deep-seated landslide

Where landslide hazard mitigation is impossible, Early Warning Systems are a valuable alternative to reduce landslide risk. Nowcasting and Early Warning Systems for landslide hazard mitigation have been implemented mostly at local scale, as such systems are often difficult to implement at regional scale or in remote areas due to dependency on fieldwork as well as local sensors. In recent years, various studies have demonstrated the effective application of machine learning for deformation forecasting of slow-moving, non-catastrophic, deep-seated landslides. Machine learning, combined with satellite remote sensing products offers new opportunities for both local and regional monitoring of deep-seated landslides and associated processes.

We tested the opportunities for machine learning on a multi-sensor monitored Austrian landslide. Our goal was to link conditions on the slope to the deformation pattern, to nowcast the deformation accelerations four days ahead of time. The in-situ sensors enabled us to test various model configurations based on combinations of local, remote sensing and retrospective analysis data sources. Our early results with shallow neural networks provide important context for future attempts. The complexities encountered were twofold: the machine learning model is poorly constrained due to the limited time span of five years of observations, and standard error metrics, like mean squared error, are unsuitable for model optimizations for landslide nowcasting.

First, even in Europe, with a six-day repeat cycle for Sentinel-1, there will be less than 500 InSAR deformation estimates from the start of the mission early 2015 to the end of 2022. As as consequence, there are only a few uniquely identifiable accelerations at the slope, and their timing is poorly defined within the six days between acquisitions. Therefore, the amount of training data is limited compared to the potentially large number of variables in more powerful machine learning models. On the Austrian slope we could rely on local, daily deformation measurements, to reveal sub-weekly minor accelerations, and to simulate potential, future, data availability.

Second, training of machine learning models is typically aimed at minimizing the average error. However, the average is a poor descriptor of the landslide accelerations that are deviations from the average, long-term behaviour. An alternative error metric was developed, that is more resiliant to slight timing errors.

Therefore, landslide deformation nowcasting is not a straightforward application of machine learning and there is a long road ahead for the large scale implementation of machine learning in landslide nowcasting and Early Warning Systems. Next step will be to evaluate our model on a landslide with a stronger deformation signal and more rapid onset of acceleration. We expect that these additional experiments will strengthen our preliminary conclusion that a successful nowcasting system requires simple, robust models and frequent, high quality and event rich data to train the system.