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Can I read Advanced KNN Approaches for Explainable Seismic-Volcanic Signal Classification on EtoBox?
Advanced KNN Approaches for Explainable Seismic-Volcanic Signal Classification by Manuele Bicego; Alberto Rossetto; Matteo Olivieri; John Makario Londoño-Bonilla; Mauricio Orozco-Alzate is a Engineering article available to read on EtoBox.
What is Advanced KNN Approaches for Explainable Seismic-Volcanic Signal Classification about?
Acquisition, classification, and analysis of seismic data are crucial tasks in volcano monitoring. The large number of seismic signals that are continuously acquired during the first monitoring stage poses a huge challenge for the human experts that must classify and analyze them. Several automatic classification systems have been proposed in the literature to alleviate such an overwhelming workload, each one characterized by different levels of accuracy, computational complexity, and interpretability. Considering this last perspective, which represents one of the recent key issues in geoscience, it is possible to find many accurate methods (in terms of classification accuracy) which however represent black boxes, not permitting a clear interpretation. On the other hand, there are other approaches, such as those based on support vector machines (SVM), random forests (RF), and K-nearest neighbor (KNN), which permit the interpretation of results, rules, and models at different levels. Among these last techniques, KNN approaches for volcanic signal classification typically do
Who reads Advanced KNN Approaches for Explainable Seismic-Volcanic Signal Classification?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Manuele Bicego; Alberto Rossetto; Matteo Olivieri; John Makario Londoño-Bonilla; Mauricio Orozco-Alzate
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Engineering (Physical Sciences)