the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Space-time landslide hazard modeling via Ensemble Neural Networks
Hakan Tanyas
Cees van Westen
Mark van der Meijde
Paul Martin Mai
Raphaël Huser
Luigi Lombardo
Abstract. Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geoscientific community developing data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the early-warning-system or to the rainfall-threshold themes. In this context, few published researches have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size (i.e., areas or volumes), has hardly ever been modeled over space and time. However, technological advancements in data-driven models have reached a level of maturity that allows to model all three components (Where, When and Size). This work takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this, we used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 × 1 km and classified/regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6-month resolution. The results are promising as our model performs satisfactorily both in the susceptibility (AUC = 0.93) and density prediction (Pearson r = 0.93) tasks. This model takes a significant distance from the common susceptibility literature, proposing an integrated framework for hazard modeling in a data-driven context.
To promote reproducibility and repeatability of the analyses in this work, we share data and codes in a github repository accessible from this https://github.com/ashokdahal/LandslideHazard.
Ashok Dahal et al.
Status: open (extended)
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RC1: 'Comment on egusphere-2023-584', Anonymous Referee #1, 06 Jun 2023
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Overview and general comments:
The authors suggest using an "Ensemble Neural Network" to holistically assess landslide hazards involving all the terms from its definition: location, time, and magnitude. They attractively demonstrate the advancement in using a data-driven model with adequate discussion, also criticizing their modelling setup, particularly the causality limitation of machine learning tools.
In my below comments, I pointed out a few minor issues to improve the manuscript and the confidence in the results—my comments mainly concern data and methods. A few of the suggested literature is redundant; authors should not feel obliged to involve them in the current work. I hope that the authors will benefit from my suggestions.
Minor comments:
The authors emphasize the modelling setup's temporal aspect (advancement) in several places. However, the abstract only presents results (performance) regarding space and magnitude.
Also, the method section does not highlight how the temporal aspect and area density is assessed. I would appreciate it if those bits of the manuscript were extended. Studying the landslide legacy effect, I find the Fan 2013 paper really useful.
Huang, R., Fan, X. The landslide story. Nature Geosci 6, 325–326 (2013). https://doi.org/10.1038/ngeo1806
Do authors smooth the curvature metrics? One option could be smoothing it to average landslide size. In the meantime, total curvature is used to compute topographic amplification of seismic signals, which correlate well with the landslide activity (e.g., Maufroy et al., 2015; von Specht et al., 2019). Authors should consider experimenting with that. The second suggested article also claims that PGV is a better metric for studying coseismic landsliding than the common PGA.
Maufroy, E., Cruz-Atienza, V. M., Cotton, F., and Gaffet, S.: Frequency-scaled curvature as a proxy for topographic site-effect amplification and ground-motion variability, Bull. Seismol. Soc. Am., 105, 354–367, https://doi.org/10.1785/0120140089, 2015.
von Specht, S., Ozturk, U., Veh, G., Cotton, F., and Korup, O.: Effects of finite source rupture on landslide triggering: the 2016 Mw 7.1 Kumamoto earthquake, Solid Earth, 10, 463–486, https://doi.org/10.5194/se-10-463-2019, 2019.
In a few places, authors mention limitations arising from data imbalance, e.g., line 209. Could they try sampling an equal amount of data from different classes and assessing accuracy?
There are several figures with 8 to 12 subplots. I found those figures rather uninformative. It is tough to get the main message of those figures. For example, success differences of the model over time are not apparent in Figure 8; residual differences are not evident and hard to see in Figure 9; differences between susceptibility and area density in-between and over time are not easy to recognize in Figure 10. I believe also the message of Figure 2 could be given differently.
Could providing correlations between subplots of Figure 11 be helpful?
Line 30: "neglecting" à I found the statement slightly judgmental. The landslide community was primarily focused on the location aspect of landslides, as temporal landslide data was rarely available, if at all.
Line 56: "Section ??"
Physics-based or Physically-based model is a better term to use? The manuscript includes both terms.
Citation: https://doi.org/10.5194/egusphere-2023-584-RC1
Ashok Dahal et al.
Data sets
Data for space time landslide hazard modelling via ensemble neural networks Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Cees van Westen, P. Martin Mai, Raphael Huser, and Luigi Lombardo https://github.com/ashokdahal/LandslideHazard
Model code and software
Code for space time landslide hazard modelling via ensemble neural networks Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Cees van Westen, P. Martin Mai, Raphael Huser, and Luigi Lombardo https://github.com/ashokdahal/LandslideHazard
Ashok Dahal et al.
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