In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of DdLSM. This research begins with an introduction of the theoretical aspects of DdLSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent, inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitative-oriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017–2020. The originality of this research is acknowledged by combining together: (a) a recap of important theoretical aspects of DdLSM; (b) a bibliometric analysis on the topic; (c) a quantitative-oriented review of relevant publications; and (d) a systematic summary of the findings, indicating important aspects and potential developments related to the DdLSM research topic. The results show that DdLSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.

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.

This study presents an initial evaluation of the heuristic landslide susceptibility map for the Municipality of Rio de Janeiro by comparing it with the official landslide inventory. The objective is to provide a first analysis of the accuracy of the current map (Reference Map), which was developed using heuristic methods, in contrast with a basic predictive model based on Generalized Additive Models (GAMs). The study includes a critical review of the existing inventory and examines landslide records from 2010 to 2016, using georeferenced data provided by the GeoRio Foundation. Data from 2017 and 2018 are used for a preliminary test of the model. Rather than proposing a replacement, this study suggests that even simple data-driven models can offer useful insights into potential improvements in the reference susceptibility map. The results are exploratory and intended to inform future, more detailed analyses. While limited in scope, this work illustrates how quantitative approaches may complement existing methods in landslide prediction assessment.