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Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in term...

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Autores principales: Sadeghi-Niaraki, Abolghasem, Kisi, Ozgur, Choi, Soo-Mi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430269/
https://www.ncbi.nlm.nih.gov/pubmed/32864200
http://dx.doi.org/10.7717/peerj.8882
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author Sadeghi-Niaraki, Abolghasem
Kisi, Ozgur
Choi, Soo-Mi
author_facet Sadeghi-Niaraki, Abolghasem
Kisi, Ozgur
Choi, Soo-Mi
author_sort Sadeghi-Niaraki, Abolghasem
collection PubMed
description This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.
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spelling pubmed-74302692020-08-27 Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods Sadeghi-Niaraki, Abolghasem Kisi, Ozgur Choi, Soo-Mi PeerJ Environmental Impacts This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it. PeerJ Inc. 2020-08-14 /pmc/articles/PMC7430269/ /pubmed/32864200 http://dx.doi.org/10.7717/peerj.8882 Text en ©2020 Sadeghi-Niaraki et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Environmental Impacts
Sadeghi-Niaraki, Abolghasem
Kisi, Ozgur
Choi, Soo-Mi
Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
title Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
title_full Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
title_fullStr Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
title_full_unstemmed Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
title_short Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
title_sort spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods
topic Environmental Impacts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430269/
https://www.ncbi.nlm.nih.gov/pubmed/32864200
http://dx.doi.org/10.7717/peerj.8882
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