Abstract
Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 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 ET0 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 ET0 values compared with the other models.
| Original language | English |
|---|---|
| Article number | e0217499 |
| Journal | PLoS ONE |
| Volume | 14 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2019 |
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All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
- General
Cite this
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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.
In: PLoS ONE, Vol. 14, No. 5, e0217499, 05.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration
AU - Ehteram, Mohammad
AU - Singh, Vijay P.
AU - Ferdowsi, Ahmad
AU - Mousavi, Sayed Farhad
AU - Farzin, Saeed
AU - Karami, Hojat
AU - Mohd, Nuruol Syuhadaa
AU - Afan, Haitham Abdulmohsin
AU - Lai, Sai Hin
AU - Kisi, Ozgur
AU - Malek, M. A.
AU - Ahmed, Ali Najah
AU - El-Shafie, Ahmed
PY - 2019/5
Y1 - 2019/5
N2 - Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 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 ET0 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 ET0 values compared with the other models.
AB - Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 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 ET0 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 ET0 values compared with the other models.
UR - http://www.scopus.com/inward/record.url?scp=85066480927&partnerID=8YFLogxK
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U2 - 10.1371/journal.pone.0217499
DO - 10.1371/journal.pone.0217499
M3 - Article
C2 - 31150443
AN - SCOPUS:85066480927
VL - 14
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 5
M1 - e0217499
ER -