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An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration
Reference evapotranspiration (ET(0)) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET(0) at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544354/ https://www.ncbi.nlm.nih.gov/pubmed/31150443 http://dx.doi.org/10.1371/journal.pone.0217499 |
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author | Ehteram, Mohammad Singh, Vijay P. Ferdowsi, Ahmad Mousavi, Sayed Farhad Farzin, Saeed Karami, Hojat Mohd, Nuruol Syuhadaa Afan, Haitham Abdulmohsin Lai, Sai Hin Kisi, Ozgur Malek, M. A. Ahmed, Ali Najah El-Shafie, Ahmed |
author_facet | Ehteram, Mohammad Singh, Vijay P. Ferdowsi, Ahmad Mousavi, Sayed Farhad Farzin, Saeed Karami, Hojat Mohd, Nuruol Syuhadaa Afan, Haitham Abdulmohsin Lai, Sai Hin Kisi, Ozgur Malek, M. A. Ahmed, Ali Najah El-Shafie, Ahmed |
author_sort | Ehteram, Mohammad |
collection | PubMed |
description | Reference evapotranspiration (ET(0)) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET(0) at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET(0) more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET(0) values compared with the other models. |
format | Online Article Text |
id | pubmed-6544354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65443542019-06-17 An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration Ehteram, Mohammad Singh, Vijay P. Ferdowsi, Ahmad Mousavi, Sayed Farhad Farzin, Saeed Karami, Hojat Mohd, Nuruol Syuhadaa Afan, Haitham Abdulmohsin Lai, Sai Hin Kisi, Ozgur Malek, M. A. Ahmed, Ali Najah El-Shafie, Ahmed PLoS One Research Article Reference evapotranspiration (ET(0)) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET(0) at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET(0) more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET(0) values compared with the other models. Public Library of Science 2019-05-31 /pmc/articles/PMC6544354/ /pubmed/31150443 http://dx.doi.org/10.1371/journal.pone.0217499 Text en © 2019 Ehteram et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ehteram, Mohammad Singh, Vijay P. Ferdowsi, Ahmad Mousavi, Sayed Farhad Farzin, Saeed Karami, Hojat Mohd, Nuruol Syuhadaa Afan, Haitham Abdulmohsin Lai, Sai Hin Kisi, Ozgur Malek, M. A. Ahmed, Ali Najah El-Shafie, Ahmed An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
title | An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
title_full | An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
title_fullStr | An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
title_full_unstemmed | An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
title_short | An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
title_sort | improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544354/ https://www.ncbi.nlm.nih.gov/pubmed/31150443 http://dx.doi.org/10.1371/journal.pone.0217499 |
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