Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects

Steger, Stefan; Mair, Volkmar; Kofler, Christian; Pittore, Massimiliano; Zebisch, Marc; Schneiderbauer, Stefan

doi:10.1016/j.scitotenv.2021.145935

Cited by 94 publications

(88 citation statements)

References 87 publications

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“…For the regression analyses, we implemented a Bayesian version of a Generalized Additive Mixed Model (GAMM, Steger et al, 2021), where a binomial likelihood is used to distinguish stable and unstable SUs, whereas a GAMM with a Gaussian likelihood is then used to model landslide planimetric extents expressed in logarithmic scale (see Lombardo et al, 2021). To evaluate the modelling performance, we use receiver operating characteristic curves (ROC) and their integrated area under the curve (AUC, Hosmer and Lemeshow, 2000).…”

Section: Characteristics Of the Landslide Eventmentioning

confidence: 99%

The world’s second-largest, recorded landslide event: lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

Tanyaş¹,

Hill²,

Mahoney³

et al. 2022

Preprint

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Widespread landslide events provide rare but valuable opportunities to investigate the spatial and size distributions of landslides in relation to seismic, climatic, geological and morphological factors. This study presents a unique event inventory for the co-seismic landslides induced by the February 25, 2018 Mw 7.5 Papua New Guinea earthquake as well as its post-seismic counterparts including the landslides triggered by either aftershocks or succeeding rainfall events that occurred between February 26 and March 19. We mapped approximately 11,500 landslides of which more than 10,000 were triggered by the mainshock with a total failed planimetric area of about 145 km2. Such a large area makes this inventory the world’s second-largest recorded landslide event after the 2008 Wenchuan earthquake. Large landslides are abundant throughout the study area located within the remote Papua New Guinea Highlands. Specifically, more than half of the landslide population is larger than 50,000 m2 and overall, post-seismic landslides are even larger than their co-seismic counterparts. Our analyses indicate that large and widespread landslides were triggered as a result of the compound effects of the strong seismicity, complex geology, steep topography and high rainfall. We statistically show that the 15-day antecedent precipitation, as a predisposing factor, contributes to the spatial distribution of co-seismic landslides. Also, we statistically demonstrate that the cumulative effect of aftershocks is the main factor disturbing steep hillslopes and causing the initiation of very large landslides up to the size of ~5 km2. Taking aside the role of the intense seismic swarm and antecedent precipitation, these inventories also provide evidence for landslide events where the active tectonics contribute to weaken hillslopes and the fatigue damage. Overall, the dataset and the findings provided by this paper is a step forward in seismic landslide hazard assessment of the entire Papua New Guinea mainland.

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Section: Characteristics Of the Landslide Eventmentioning

confidence: 99%

The world’s second-largest, recorded landslide event: lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

Tanyaş¹,

Hill²,

Mahoney³

et al. 2022

Preprint

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show abstract

“…slope and land cover). Therefore, the usage of bias-describing variables as random intercepts enables to account for the associated variation during model parameter estimation (Steger et al, 2021), while the effects of these same variables are averaged-out for the predictions. For this study, the two categorical variables, land cover and mapping domain, which mainly describe a systematic incompleteness of landslide information, were introduced as random intercepts to reduce associated confounding and direct bias propagation.…”

Section: Statistical Modellingmentioning

confidence: 99%

Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria

2021

Self Cite

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The reliability of input data to be used within statistically based landslide susceptibility models usually determines the quality of the resulting maps. For very large territories, landslide susceptibility assessments are commonly built upon spatially incomplete and positionally inaccurate landslide information. The unavailability of flawless input data is contrasted by the need to identify landslide-prone terrain at such spatial scales. Instead of simply ignoring errors in the landslide data, we argue that modellers have to explicitly adopt their modelling design to avoid misleading results. This study examined different modelling strategies to reduce undesirable effects of error-prone landslide inventory data, namely systematic spatial incompleteness and positional inaccuracies. For this purpose, the Austrian territory with its abundant but heterogeneous landslide data was selected as a study site. Conventional modelling practices were compared with alternative modelling designs to elucidate whether an active counterbalancing of flawed landslide information can improve the modelling results. In this context, we compared widely applied logistic regression with an approach that allows minimizing the effects of heterogeneously complete landslide information (i.e. mixed-effects logistic regression). The challenge of positionally inaccurate landslide samples was tackled by elaborating and comparing the models for different terrain representations, namely grid cells, and slope units. The results showed that conventional logistic regression tended to reproduce incompleteness inherent in landslide training data in case the underlying model relied on explanatory variables directly related to the data bias. The adoption of a mixed-effects modelling approach appeared to reduce these undesired effects and led to geomorphologically more coherent spatial predictions. As a consequence of their larger spatial extent, the slope unit–based models were able to better cope with positional inaccuracies of the landslide data compared to their grid-based equals. The presented research demonstrates that in the context of very large area susceptibility modelling (i) ignoring flaws in available landslide data can lead to geomorphically incoherent results despite an apparent high statistical performance and that (ii) landslide data imperfections can actively be diminished by adjusting the research design according to the respective input data imperfections.

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“…Джазаиров-Кахраманов А.В., Имамбеков О., Карипбаева Л.Т., Стеблякова А.А. 8 Li(p,у) 9 Be ҚАРМАУЫ КЕЗІНДЕ СӘЙКЕС 9…”

Section: мазмұныunclassified

“…According to Italian scientists in [8], classical mathematical models of landslide processes are based on biased data. Each model addresses only part of the key factors, which leads to no uniformity in the results of landslide's forecasts of the models.…”

mentioning

confidence: 99%

Data Collection for Intellectual Forecasting: Metthods and Results

Zhantayev¹,

Kairanbayeva²,

Kiyalbayev³

et al. 2021

SPM

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Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects

Cited by 94 publications

References 87 publications

The world’s second-largest, recorded landslide event: lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

The world’s second-largest, recorded landslide event: lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria

Data Collection for Intellectual Forecasting: Metthods and Results

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