What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Machine learning
Alexander Brenning mainly focuses on Statistics, Rock glacier, Generalized additive model, Support vector machine and Permafrost.
The various areas that Alexander Brenning examines in his Rock glacier study include Hydrology and Digital elevation model.
Alexander Brenning combines subjects such as Landslide and Random forest with his study of Generalized additive model.
His Support vector machine study integrates concerns from other disciplines, such as Resampling, Spatial analysis and Feature selection.
Alexander Brenning has included themes like Bedrock, Debris, Physical geography and Statistical model in his Permafrost study.
His work carried out in the field of Artificial intelligence brings together such families of science as Data mining and Receiver operating characteristic.
His most cited work include:
- Spatial prediction models for landslide hazards: review, comparison and evaluation (383 citations)
- Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling (251 citations)
- Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling (251 citations)
What are the main themes of his work throughout his whole career to date?
Alexander Brenning mainly investigates Rock glacier, Permafrost, Geomorphology, Landslide and Physical geography.
The study incorporates disciplines such as Hydrology, Terrain, Snow and Digital elevation model in addition to Rock glacier.
His Permafrost research includes elements of Climatology, Climate change, Statistical model, Bedrock and Spatial distribution.
The concepts of his Landslide study are interwoven with issues in Statistics, Generalized additive model, Receiver operating characteristic and Land use.
His Statistics study combines topics in areas such as Random forest and Support vector machine.
The Random forest study combines topics in areas such as Arid, Lasso and Satellite imagery.
He most often published in these fields:
- Rock glacier (21.85%)
- Permafrost (18.54%)
- Geomorphology (17.22%)
What were the highlights of his more recent work (between 2017-2021)?
- Landslide (19.87%)
- Physical geography (15.23%)
- Climate change (9.27%)
In recent papers he was focusing on the following fields of study:
Alexander Brenning mostly deals with Landslide, Physical geography, Climate change, Climatology and Structural basin.
His study in the fields of Landslide susceptibility under the domain of Landslide overlaps with other disciplines such as Commercial software.
Alexander Brenning has researched Climate change in several fields, including Spatial distribution, Streamflow and Precipitation.
His Snow research includes themes of Permafrost, Remote sensing and Scale.
His Scale research integrates issues from Rock glacier and Data mining.
His Data mining research includes themes of Terrain and Support vector machine.
Between 2017 and 2021, his most popular works were:
- Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data (44 citations)
- Trends and variability in streamflow and snowmelt runoff timing in the southern Tianshan Mountains (26 citations)
- Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave (22 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Machine learning
His primary scientific interests are in Structural basin, Physical geography, Remote sensing, Structure from motion and Landslide.
Alexander Brenning interconnects Thunderstorm, Land cover, Land use, Generalized additive model and Environmental change in the investigation of issues within Structural basin.
His Physical geography research focuses on subjects like Hydropower, which are linked to Climate change, Precipitation, Flood myth and Streamflow.
When carried out as part of a general Remote sensing research project, his work on Photogrammetry is frequently linked to work in High spatial resolution, therefore connecting diverse disciplines of study.
He works mostly in the field of Landslide, limiting it down to topics relating to Lidar and, in certain cases, Terrain and Support vector machine.
The various areas that he examines in his Support vector machine study include Hyperparameter, Random forest and Scale.
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