Research Article: An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration

Date Published: May 31, 2019

Publisher: Public Library of Science

Author(s): Mohammad Ehteram, Vijay P. Singh, Ahmad Ferdowsi, Sayed Farhad Mousavi, Saeed Farzin, Hojat Karami, Nuruol Syuhadaa Mohd, Haitham Abdulmohsin Afan, Sai Hin Lai, Ozgur Kisi, M. A. Malek, Ali Najah Ahmed, Ahmed El-Shafie, Jie Zhang.


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.

Partial Text

This study deals with a station in Pantangar, India, located at (79038’0”, 2900’0”), as shown in Fig 6. The station is located in the central Himalayan area of India, and experiences an average rainfall of 1,400 mm per year. Information collected from a weather center site in the area was used to simulate monthly ET. This included Tmin,Tmax (maximum and minimum temperature), RH1,RH2 (relative humidity; the RH1 was recorded at 7 AM and RH2 was recorded at 2 PM), Sw (wind speed), Hss (sunshine hours) and EPm (monthly ET0). Meteorological tools were considered to collect data at a meteorological observatory or weather station. There are many weather stations in India, which are regulated by Indian meteorological department. The meteorological data were obtained from weather stations in the current study. Previous hydrological studies consider three levels for the investigating of models [1,2]. The first level is known as training level to prepare the method and obtain the parameters and structure of method. The second level is related to the validation, and the third level is related to the test level so that the ability of models are determined based on the application of the model on the data of this period. The longer period is allocated to the training level as the decision maker can prepare the method well and then the remaining of periods are used for the verification and calibration levels [1,2,8].

One of the major keys to constructing adequate plans for agricultural water and irrigation management is accurate estimation of ET0. The current study presents a new method based on SVM and CA for the simulation of monthly ET0. To assess the performance of the proposed simulation model, the model was examined using ET0 data from India. Different scenarios for the simulation model were investigated with different alternative combinations of maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours. The results showed that the proposed SVM-CA model was more accurate than the GP, ANFIS and M5T models for simulating ET0. The proposed ET0 simulation model based on the SVM-CA method successfully reduced the RMSE and MAE by 5–15% and 5–17%, respectively, in the testing stage compared with GP. Furthermore, the study found that the best scenario combination included Tmin,Tmax,RH1,RH2,Sw,Hss as inputs, which resulted in superior performance over all other models. The worst scenario performance considered only maximum and minimum temperatures as inputs. The M5T model supplied the worst performance out of all the models, and as a result, is not recommended for simulating ET0.




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