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^*

^*Tämän työn vastaava kirjoittaja

Geologia ja mineralogia

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Advances in Computational Intelligence
Alaotsikko	17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings
Toimittajat	Ignacio Rojas, Gonzalo Joya, Andreu Catala
Kustantaja	Springer Science and Business Media Deutschland GmbH
Sivut	614-625
Sivumäärä	12
ISBN (painettu)	9783031430848
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-031-43085-5_49
Tila	Julkaistu - 2023
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	17th International Work-Conference on Artificial Neural Networks, IWANN 2023 - Ponta Delgada, Portugal Kesto: 19 kesäk. 2023 → 21 kesäk. 2023

Julkaisusarja

Nimi	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta	14134 LNCS
ISSN (painettu)	0302-9743
ISSN (elektroninen)	1611-3349

Konferenssi

Konferenssi	17th International Work-Conference on Artificial Neural Networks, IWANN 2023
Maa/Alue	Portugal
Kaupunki	Ponta Delgada
Ajanjakso	19/06/23 → 21/06/23

Pääsy asiakirjaan

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

Muut tiedostot ja linkit

Linkki julkaisuun Scopuksessa

Viittausmuodot

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

@inproceedings{8f8b888f07194e7599aa7403a6e131ae,

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 = "9783031430848",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "614--625",

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

booktitle = "Advances in Computational Intelligence",

}

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. julkaisussa I Rojas, G Joya & A Catala (toim), 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), Vuosikerta 14134 LNCS, Springer Science and Business Media Deutschland GmbH, Sivut 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. toim. / 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); Vuosikerta 14134 LNCS).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

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 - http://www.scopus.com/inward/record.url?scp=85174525107&partnerID=8YFLogxK

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

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

M3 - Published conference proceeding

AN - SCOPUS:85174525107

SN - 9783031430848

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

A2 - Rojas, Ignacio

A2 - Joya, Gonzalo

A2 - Catala, Andreu

PB - Springer Science and Business Media Deutschland GmbH

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. julkaisussa Rojas I, Joya G, Catala A, toimittajat, 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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