Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines
Year of publication
2023
Authors
Atsemegiorgis, Tamirat; Espinosa-Leal, Leonardo; Lendasse, Amaury; Mattbäck, Stefan; Björk, Kaj-Mikael; Akusok, Anton
Abstract
<p>This paper explores the performance of the Extreme Learning Machine (ELM) in an acid sulfate soil classification task. ELM is an Artificial Neuron Network with a new learning method. The dataset comes from Finland’s west coast region, containing point observations and environmental covariates datasets. The experimental results show similar overall accuracy of ELM and Random Forest models. However, ELM implementation is easy, fast, and requires minimal human intervention compared to conventional ML methods like Random Forest.</p>
Show moreOrganizations and authors
Geological Survey of Finland
Mattbäck Stefan
Publication type
Publication format
Article
Parent publication type
Conference
Article type
Other article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A4 Article in conference proceedingsPublication channel information
Journal/Series
Parent publication name
Publisher
Pages
614-625
ISSN
ISBN
Publication forum
Publication forum level
1
Open access
Open access in the publisher’s service
No
Self-archived
No
Other information
Fields of science
Computer and information sciences; Other engineering and technologies; Geosciences
Keywords
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Publication country
Germany
Internationality of the publisher
International
Language
English
International co-publication
Yes
Co-publication with a company
No
DOI
10.1007/978-3-031-43085-5_49
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes