The Field Geomorphologist in a Time of Artificial Intelligence and Machine Learning

Bibliographic Data

ID5154207
AuthorsChris Houser (0000-0002-7880-7619, University of Windsor), Jacob Lehner (University of Windsor), Alex Smith (0000-0002-4841-4961, University of Windsor)
Year2022
Volume112
Issue5
Pages1260-1277
Publication date2022-07-04
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the American Association of Geographers (JOURNAL)
Journal identifiersISSN: 2469-4452 • E-ISSN: 2469-4460
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/24694452.2021.1985956
OpenAlexW4205510294
LanguageEN
Citations received6
References cited124

An increasing number of papers incorporate machine learning (ML) approaches to analyze spatially and temporally rich data sets in geomorphology. These data-driven approaches have the potential to significantly improve our understanding of complex systems across a range of scales and support the development of new theories of landform and landscape development that can eventually be incorporated into predictive models. Coupled with the growing availability of remotely sensed data, geomorphology could move further toward a desk-based science and erosion of the field tradition. Using examples from coastal geomorphology, this review of ML applications argues that the development of models that are scalable and can be translated between sites is dependent on experience in the field. Although ML models are shown to be effective as a surrogate to process-based numerical models, they are only as good as our conceptual understanding of landform and landscape form and evolution. This means that ML is simply a new and powerful tool in the proverbial belt of the geomorphologist and should not come at the expense of the field tradition that informs us of whether ML results are accurate, transferable, and scalable

Data science · Desk · Geomorphology · Interpretability · Landform · Machine learning · Scalability · Coastal and Marine Dynamics · Coastal wetland ecosystem dynamics · Computer Science · Flood Risk Assessment and Management · Mathematics · Artificial Intelligence · Geology

Tools

Open DOI