Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran
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VIEW THE REPLYPublication: Journal of Irrigation and Drainage Engineering
Volume 146, Issue 6
Abstract
Evapotranspiration estimation and forecasting is a key step in water management projects, especially in water-scarce countries such as Iran. Seasonal autoregressive integrated moving average (SARIMA), support vector machine (SVM), and group method of data handling (GMDH) models were developed and assessed to find an appropriate model for short and long-term forecasting of monthly reference evapotranspiration in the Guilan Plain, northern Iran. Monthly meteorological data gathered from four weather stations (Anzali, Astara, Manjil, and Rasht) were used to calculate monthly reference evapotranspiration in the period of 1993–2014 using the FAO-56 Penman–Monteith (FAO-PM) equation. The evapotranspiration data from 1993 to 2012 were used to fit SARIMA models and calibrate SVM and GMDH models, and the monthly evapotranspiration rates for the years 2013 and 2014 were forecasted using the calibrated models. The developed models were assessed using RMS error (RMSE), the Pearson correlation coefficient (), the Nash–Sutcliffe model efficiency coefficient (NS), and percent bias. Taylor diagrams also were used to compare the accuracy of forecasts produced by the models. For the whole forecasting period (2013–2014), the RMSE of the calibrated SARIMA, SVM, and GMDH models were, respectively, 8.796, 9.830, and for Anzali weather station; 8.136, 9.057, and for Astara weather station; 9.454, 8.947, and for Manjil weather station; and 9.301, 10.509, and for Rasht weather station. In other words, in two weather stations under study (Anzali and Rasht), the best results were obtained from SARIMA; however, for Astara and Manjil weather stations, GMDH generated the best forecasts. Furthermore, at different forecasting horizons (1–24 months), the SARIMA models generally outperformed the SVM and GMDH models.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The authors thank the Iran Meteorological Organization for providing the data used in this study, and the reviewers and the editors who gave valuable time to review the manuscript.
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Journal of Irrigation and Drainage Engineering
Volume 146 • Issue 6 • June 2020
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©2020 American Society of Civil Engineers.
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Received: Dec 20, 2018
Accepted: Jan 2, 2020
Published online: Mar 30, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 30, 2020
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