1. Early Warning Practice for Shallow Landslides in Norway and Physical Modelling Strategies Supported by IoT-Based Monitoring

The adverse effect of climate change on society is expected to become more obvious as the number of landslides increases due to climate change. In particular, shallow landslides, are often rainfall-induced and they pose a great risk due to their high frequency and catastrophic consequences. In the conventional strategy to deal with the landslide risk, a great proportion of the budget goes into the response, reconstruction and rehabilitation, while only a small fraction is utilized for assessment and prediction to mitigate the risk. However, with the recent development of sensory technology, new possibilities are opened to monitoring and early warning system (EWS)-based landslide risk management. Additionally, EWS is often the only feasible strategy in managing the landslide risks due to the strategies relying on planning or engineering solutions being too costly or impracticable with respect to the elements at risk. In the EWS strategy, the warning can be based on certain threshold values relating to the triggering variables (e.g. rainfall intensity, displacement), risk estimates (e.g. economic losses) or safety measures (e.g. factor of safety). In this study, the current state of the art of early warning practice for shallow landslides on a regional to national scale in Norway will be discussed. The capacity of physical-based models to improve the existing EWS in Norway will be examined in a case study. In addition, a brief overview of the Internet of Things (IoT) and the adoption of IoT-based monitoring will be provided to evaluate their capacity to revolutionize the current state of the art of EWS.