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Alexander Brenning
Researcher at University of Jena
Publications - 129
Citations - 5746
Alexander Brenning is an academic researcher from University of Jena. The author has contributed to research in topics: Rock glacier & Landslide. The author has an hindex of 34, co-authored 116 publications receiving 4380 citations. Previous affiliations of Alexander Brenning include Schiller International University & University of Erlangen-Nuremberg.
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Spatial prediction models for landslide hazards: review, comparison and evaluation
TL;DR: In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities.
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Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling
Jason Goetz,Jason Goetz,Jason Goetz,Alexander Brenning,Alexander Brenning,Helene Petschko,Philip Leopold +6 more
TL;DR: A comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling is presented and it is suggested that the framework of this model evaluation approach can be applied to assist in selection of a suitable landslide susceptibility modeled technique.
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A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management
TL;DR: The geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring.
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Integrating physical and empirical landslide susceptibility models using generalized additive models
TL;DR: In this article, a generalized additive model (GAM) and a logistic regression model (GLM) were used to combine physical landslide models, terrain attributes and land use data.
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Permafrost distribution in the European Alps: calculation and evaluation of an index map and summary statistics
TL;DR: In this paper, a unified statistical model that is based on Alpine-wide permafrost observations is used for debris and bedrock surfaces across the entire Alps, and the model is applied to make model predictions for topographic and geomorphic conditions that differ from the terrain features used for model fitting.