Acid Sulfate Soils Classification and Prediction from Environmental Covariates using Extreme Learning Machines
Abstract
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.
Recommended Citation
T. Atsemegiorgis et al., "Acid Sulfate Soils Classification and Prediction from Environmental Covariates using Extreme Learning Machines," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14134 LNCS, pp. 614 - 625, Springer, Jan 2023.
The definitive version is available at https://doi.org/10.1007/978-3-031-43085-5_49
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Acid Sulfate Soil; ELM; Environmental Covariate
International Standard Book Number (ISBN)
978-303143084-8
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2023
