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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
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.
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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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