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^*

^*Korresponderande författare för detta arbete

Geologi och mineralogi

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

Sammanfattning

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.

Originalspråk	Engelska
Titel på värdpublikation	Advances in Computational Intelligence
Undertitel på värdpublikation	17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings
Redaktörer	Ignacio Rojas, Gonzalo Joya, Andreu Catala
Förlag	Springer Science and Business Media Deutschland GmbH
Sidor	614-625
Antal sidor	12
ISBN (tryckt)	9783031430848
DOI	https://doi.org/10.1007/978-3-031-43085-5_49
Status	Publicerad - 2023
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	17th International Work-Conference on Artificial Neural Networks, IWANN 2023 - Ponta Delgada, Portugal Varaktighet: 19 juni 2023 → 21 juni 2023

Publikationsserier

Namn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volym	14134 LNCS
ISSN (tryckt)	0302-9743
ISSN (elektroniskt)	1611-3349

Konferens

Konferens	17th International Work-Conference on Artificial Neural Networks, IWANN 2023
Land/Territorium	Portugal
Ort	Ponta Delgada
Period	19/06/23 → 21/06/23

Åtkomst av dokument

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

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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. I I. Rojas, G. Joya, & A. Catala (Red.), Advances in Computational Intelligence: 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings (s. 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. redaktör / Ignacio Rojas ; Gonzalo Joya ; Andreu Catala. Springer Science and Business Media Deutschland GmbH, 2023. s. 614-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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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. i I Rojas, G Joya & A Catala (red), 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, s. 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. red. / Ignacio Rojas; Gonzalo Joya; Andreu Catala. Springer Science and Business Media Deutschland GmbH, 2023. s. 614-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14134 LNCS).

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › 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

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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. I Rojas I, Joya G, Catala A, redaktörer, Advances in Computational Intelligence: 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. s. 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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