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

• Published in: PloS one Vol. 14; no. 5; p. e0217499
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31-05-2019; Public Library of Science (PLoS)
• Subjects: Adaptive systems; Agricultural Irrigation; Agriculture; Algorithms; Artificial intelligence; Artificial neural networks; Biology and Life Sciences; Civil engineering; Computer and Information Sciences; Computer simulation; Earth Sciences; Environmental Monitoring - methods; Evapotranspiration; Fuzzy Logic; Fuzzy systems; Genetic algorithms; Hydraulics; Irrigation; Mathematical functions; Maximum temperatures; Methods; Minimum temperatures; Mutation; Neural networks; Physical Sciences; Relative humidity; Research and Analysis Methods; Rivers; Root-mean-square errors; Simulation; Support Vector Machine; Support vector machines; Temperature; Weather stations; Wind; Wind speed; Iran; Selangor Malaysia; United States > US; Texas; Kuala Lumpur Malaysia; Malaysia; Malaya
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0217499

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.