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

Geology and Mineralogy

Research output: Chapter in Book/Conference proceeding › Conference contribution › 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
Subtitle of host publication	17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings
Editors	Ignacio Rojas, Gonzalo Joya, Andreu Catala
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	614-625
Number of pages	12
ISBN (Print)	9783031430848
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

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14134 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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

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). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14134 LNCS). Springer Science and Business Media Deutschland GmbH. 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 Science and Business Media Deutschland GmbH, 2023. pp. 614-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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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",

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

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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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14134 LNCS, Springer Science and Business Media Deutschland GmbH, 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 Science and Business Media Deutschland GmbH, 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/Conference proceeding › Conference contribution › Scientific › peer-review

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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.

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BT - Advances in Computational Intelligence

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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 Science and Business Media Deutschland GmbH. 2023. p. 614-625. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43085-5_49

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

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