Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines

Tamirat Atsemegiorgis; Leonardo Espinosa-Leal; Amaury Lendasse; Stefan Mattbäck; Kaj Mikael Björk; Anton Akusok

doi:10.1007/978-3-031-43085-5_49

Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines

Tamirat Atsemegiorgis
, Leonardo Espinosa-Leal
, Amaury Lendasse
, Stefan Mattbäck
, Kaj Mikael Björk
, Anton Akusok^*

^*Corresponding author for this work

Not published at VTT

Arcada University of Applied Sciences
University of Houston
Åbo Akademi University
Geological Survey of Finland (GTK)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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.

Original language	English
Title of host publication	Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings
Editors	Ignacio Rojas, Gonzalo Joya, Andreu Catala
Publisher	Springer
Pages	614-625
Number of pages	12
ISBN (Electronic)	978-3-031-43085-5
ISBN (Print)	978-3-031-43084-8
DOIs	https://doi.org/10.1007/978-3-031-43085-5_49
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	17th International Work-Conference on Artificial Neural Networks, IWANN 2023 - Ponta Delgada, Portugal Duration: 19 Jun 2023 → 21 Jun 2023

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14134 LNCS
ISSN	0302-9743

Conference

Conference	17th International Work-Conference on Artificial Neural Networks, IWANN 2023
Country/Territory	Portugal
City	Ponta Delgada
Period	19/06/23 → 21/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Acid Sulfate Soil
ELM
Environmental Covariate

Access to Document

10.1007/978-3-031-43085-5_49

Cite this

Atsemegiorgis, T., Espinosa-Leal, L., Lendasse, A., Mattbäck, S., Björk, K. M., & Akusok, A. (2023). Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings (pp. 614-625). Springer. https://doi.org/10.1007/978-3-031-43085-5_49

Atsemegiorgis, Tamirat ; Espinosa-Leal, Leonardo ; Lendasse, Amaury et al. / Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines. Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings. editor / Ignacio Rojas ; Gonzalo Joya ; Andreu Catala. Springer, 2023. pp. 614-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14134 LNCS).

@inproceedings{493fe7178a1e46a190fa02919a31cd38,

title = "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{\textquoteright}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.",

keywords = "Acid Sulfate Soil, ELM, Environmental Covariate",

author = "Tamirat Atsemegiorgis and Leonardo Espinosa-Leal and Amaury Lendasse and Stefan Mattb{\"a}ck and Bj{\"o}rk, \{Kaj Mikael\} and Anton Akusok",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Work-Conference on Artificial Neural Networks, IWANN 2023 ; Conference date: 19-06-2023 Through 21-06-2023",

year = "2023",

doi = "10.1007/978-3-031-43085-5\_49",

language = "English",

isbn = "978-3-031-43084-8",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "614--625",

editor = "Ignacio Rojas and Gonzalo Joya and Andreu Catala",

booktitle = "Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings",

address = "Germany",

}

Atsemegiorgis, T, Espinosa-Leal, L, Lendasse, A, Mattbäck, S, Björk, KM & Akusok, A 2023, Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines. in I Rojas, G Joya & A Catala (eds), Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14134 LNCS, pp. 614-625, 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, 19/06/23. https://doi.org/10.1007/978-3-031-43085-5_49

Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines. / Atsemegiorgis, Tamirat; Espinosa-Leal, Leonardo; Lendasse, Amaury et al.
Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings. ed. / Ignacio Rojas; Gonzalo Joya; Andreu Catala. Springer, 2023. p. 614-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14134 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines

AU - Atsemegiorgis, Tamirat

AU - Espinosa-Leal, Leonardo

AU - Lendasse, Amaury

AU - Mattbäck, Stefan

AU - Björk, Kaj Mikael

AU - Akusok, Anton

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Acid Sulfate Soil

KW - ELM

KW - Environmental Covariate

UR - https://www.scopus.com/pages/publications/85174525107

U2 - 10.1007/978-3-031-43085-5_49

DO - 10.1007/978-3-031-43085-5_49

M3 - Conference article in proceedings

AN - SCOPUS:85174525107

SN - 978-3-031-43084-8

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 614

EP - 625

BT - Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings

A2 - Rojas, Ignacio

A2 - Joya, Gonzalo

A2 - Catala, Andreu

PB - Springer

T2 - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023

Y2 - 19 June 2023 through 21 June 2023

ER -

Atsemegiorgis T, Espinosa-Leal L, Lendasse A, Mattbäck S, Björk KM, Akusok A. Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines. In Rojas I, Joya G, Catala A, editors, Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings. Springer. 2023. p. 614-625. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14134 LNCS). doi: 10.1007/978-3-031-43085-5_49

Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this