70-years-of-machine-learning-in-geoscience-in-review
Code to recreate the tutorial and figures in the book chapter.
https://github.com/jesperdramsch/70-years-of-machine-learning-in-geoscience-in-review
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (2.6%) to scientific vocabulary
Last synced: 6 months ago
·
JSON representation
·
Repository
Code to recreate the tutorial and figures in the book chapter.
Basic Info
- Host: GitHub
- Owner: JesperDramsch
- License: mit
- Language: HTML
- Default Branch: master
- Size: 725 KB
Statistics
- Stars: 36
- Watchers: 5
- Forks: 8
- Open Issues: 0
- Releases: 0
Created over 5 years ago
· Last pushed almost 4 years ago
Metadata Files
Readme
License
Citation
README.md
70-Years-of-Machine-Learning-in-Geoscience-in-Review
Code to recreate the tutorial and figures in the book chapter.
Find the preprint at: https://arxiv.org/abs/2006.13311
Book_Examples.ipynb contains the tutorials and resulting visualizations of the chapter.
Book_Visualizations.ipynb contains programmatically generated figures for the chapter.
The generated figures are stored in figures/ the movie through the 3D decision boundary volume is stored externally at:
Dramsch, Jesper Soeren (2020): 3D decision volume of SVM, Random Forest, and Deep Neural Network. figshare. Media. https://doi.org/10.6084/m9.figshare.12640226
Owner
- Name: Jesper Dramsch
- Login: JesperDramsch
- Kind: user
- Location: Bonn
- Company: @ECMWF
- Website: dramsch.net
- Repositories: 101
- Profile: https://github.com/JesperDramsch
Scientist for Machine Learning. 🦾 No step on snek. 🐍 You miss 99% of the benchmarks you don't overfit on.
Citation (CITATION.cff)
cff-version: '1.2.0'
message: 'Please cite the following works when using this software.'
abstract: 'Code to recreate the tutorial and figures in the book chapter.'
authors:
- family-names: 'Dramsch'
given-names: 'Jesper Sören'
orcid: '0000-0001-8273-905X'
identifiers:
- type: 'url'
value: 'https://github.com/JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review'
title: 'JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review'
url: 'https://github.com/JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review'
abbreviation: '70-Years-of-Machine-Learning-in-Geoscience-in-Review'
date-published: 2020-07-17
year: 2020
month: 7
type: 'software'
preferred-citation:
abstract: 'This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the codevelopments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging toward a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades. Furthermore, this chapter explores the shift from mathematical fundamentals and knowledge in software development toward skills in model validation, applied statistics, and integrated subject matter expertise. The review is interspersed with code examples to complement the theoretical foundations and illustrate model validation and machine learning explainability for science. The scope of this review includes various shallow machine learning methods, e.g., decision trees, random forests, support-vector machines, and Gaussian processes, as well as, deep neural networks, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. Regarding geoscience, the review has a bias toward geophysics but aims to strike a balance with geochemistry, geostatistics, and geology, however, excludes remote sensing, as this would exceed the scope. In general, I aim to provide context for the recent enthusiasm surrounding deep learning with respect to research, hardware, and software developments that enable successful application of shallow and deep machine learning in all disciplines of Earth science.'
authors:
- family-names: 'Dramsch'
given-names: 'Jesper Sören'
orcid: '0000-0001-8273-905X'
doi: 'https://doi.org/10.1016/bs.agph.2020.08.002'
identifiers:
- type: 'doi'
value: 'https://doi.org/10.1016/bs.agph.2020.08.002'
- type: 'url'
value: 'https://www.sciencedirect.com/science/article/pii/S0065268720300054'
- type: 'other'
value: 'urn:issn:0065-2687'
title: 'Chapter One - 70 years of machine learning in geoscience in review'
url: 'https://www.sciencedirect.com/science/article/pii/S0065268720300054'
type: 'book-chapter'
GitHub Events
Total
- Watch event: 3
Last Year
- Watch event: 3
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Jesper Dramsch | j****r@d****t | 12 |
Committer Domains (Top 20 + Academic)
dramsch.net: 1
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0