Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran-Reference-Cited by-同舟云学术

Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran

Published:2020-06 Issue:6 Volume:146 Page:04020010
ISSN:0733-9437
Container-title:Journal of Irrigation and Drainage Engineering
language:en
Short-container-title:J. Irrig. Drain Eng.

Author:

Ashrafzadeh Afshin¹^ORCID,Kişi Ozgur²,Aghelpour Pouya³^ORCID,Biazar Seyed Mostafa⁴^ORCID,Masouleh Mohammadreza Askarizad⁵^ORCID

Affiliation:

1. Associate Professor, Faculty of Agricultural Sciences, Dept. of Water Engineering, Univ. of Guilan, Khalij-e-Fars Blvd., Rasht 41996-13776, Iran (corresponding author). ORCID: .

2. Professor, Faculty of Natural Sciences and Engineering, Ilia State Univ., Kakutsa Cholokashvili Ave. 3/5, Tbilisi 0162, Georgia.

3. Ph.D. Student, Agricultural Meteorology, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, Dept. of Irrigation and Reclamation Engineering, Univ. of Tehran, Karaj 77871-31587, Iran. ORCID: .

4. Ph.D. Candidate, Dept. of Water Engineering, Univ. of Tabriz, 29 Bahman St., Tabriz 51666-16471, Iran. ORCID: .

5. M.Sc. Student, Dept. of Computer Engineering, Sharif Univ. of Technology, Azadi St., Tehran 11155-11365, Iran. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Agricultural and Biological Sciences (miscellaneous),Water Science and Technology,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IR.1943-4774.0001471

Reference51 articles.

1. Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes

2. Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA

3. Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series