title: Earthquake-induced landslide forecast tool for New Zealand: version 2.0
Creators: name: Massey, Chris I.
name: Lukovic, Biljana
name: Husu, Rand
name: Buxton, Rob
name: Potter, Sally H.
Publisher: name: GNS Science
Year: 2021

description: This report outlines a prototype earthquake-induced landslide (EIL) forecast tool (Version 2.0) for New Zealand. This tool will produce outputs for the GeoNet landslide duty officers and Geohazard Analysts (within the National Geohazard Monitoring Centre) after a significant earthquake in near-real time – approximately 5–7 minutes after being triggered and based on the speed of the computer used. A ‘tool’ in the context of this report is defined as software written to carry out a function based on an input. The function of this tool is to provide rapid advisory information about the ‘intensity’ and likely location of landslides following a major earthquake in New Zealand. Ground shaking data recorded by the GeoNet strong motion instrument network is currently used as the input for the tool. Landslide information will also be useful for emergency managers; regional and district councils; infrastructure managers; major landowners, such as the Department of Conversation; insurers, such as the Earthquake Commission; and the public. The tool is not intended to forecast the precise location of landslides but rather to provide responders with information that would help them focus post-earthquake reconnaissance to aid those people affected as quickly as possible. The present tool is not the definitive version; it is intended to be a working prototype, which can be used as a basis for discussions with other technical specialists and interested parties. It was used to elicit feedback from end-user groups on their needs and uses of the tool. The findings from this engagement have started to be incorporated into the tool and form the basis of future iterations. The current tool uses two deep learning statistical models to forecast the spatial distribution of landslide probability over an area. The models estimate landslide probability (0 to 1, where 1 = landslide occurrence) at the regional scale, defined as being 1:25,000 to 1:250,000. The two statistical methods are: (1) logistic regression and (2) a deep-learning neural network. Both methods adopt variations of the same input ‘training’ EIL datasets from the MW 7.8 2016 Kaikōura Earthquake, the MW 7.1 1969 Inangahua Earthquake and the MW 7.3 1929 Murchison Earthquake. In the future, the models will be trained on additional datasets from other earthquakes. Overall, the statistical models developed here provide a robust forecast of landslide probability at different levels of earthquake peak ground acceleration. However, the results from the models have a low pseudo R2, indicating that they cannot give accurate forecasts for individual grid cells. They may nevertheless give good overall estimates of the scale ‘intensity’ of landslide occurrence to be expected in individual earthquakes. Differences between future earthquake-induced ground shaking and those that the models are trained on could cause the models to perform badly, however. Given these constraints, the current models and tool are statistically robust to be used to forecast landslide intensity and locations in the minutes after a major earthquake. Feedback from stakeholder engagement is ongoing, with further areas of transdisciplinary research relating to the communication of the model outputs identified (auth)