Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA
Aghelpour, Pouya ; Mohammadi, Babak LU
and Biazar, Seyed Mostafa
(2019)
In Theoretical and Applied Climatology
138(3-4).
p.1471-1480
- Abstract
Temporal changes of the global surface temperature have been used as a prominent indicator of global climate change; therefore, making dependable forecasts underlies the foundation of sound environmental policies. In this research, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with Firefly optimization algorithm (SVR-FA) as a meta-innovative model, in long-term forecasting of average monthly temperature. For this, 5 stations from different climates of Iran (according to the Extended De Martonne method) were selected, including Abadan, Anzali, Isfahan, Mashhad, and Tabriz. The data were collected during... (More)
Temporal changes of the global surface temperature have been used as a prominent indicator of global climate change; therefore, making dependable forecasts underlies the foundation of sound environmental policies. In this research, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with Firefly optimization algorithm (SVR-FA) as a meta-innovative model, in long-term forecasting of average monthly temperature. For this, 5 stations from different climates of Iran (according to the Extended De Martonne method) were selected, including Abadan, Anzali, Isfahan, Mashhad, and Tabriz. The data were collected during 1951–2011, for training (75%) and testing (25%). After selecting the best models, the average monthly temperature has been forecasted for the period 2012–2017. The results showed that the models had better performances in Extra-Arid and Warm (Abadan) and after that Extra-Arid and Cold (Isfahan) climate, in long-term forecasting. The weakest performances of the models were reported in Semi-Arid and Cold climate, including Mashhad and Tabriz. Also, despite the use of the non-linear SVR model and its meta-innovative type, SVR-FA, the results showed that, in the climates of Iran, the linear and classical SARIMA model still offers a more appropriate performance in temperature long-term forecasting. So that it could forecast the average monthly temperature of Abadan with root mean square error (RMSE) = 1.027 °C, and Isfahan with RMSE = 1.197 °C for the 6 years ahead. The SVR and SVR-FA models also had good performances. The results of this checking also report the effectiveness of the merging SVR model with the Firefly optimization algorithm in temperature forecasting in Iran’s climates, so, compared with the SVR model, it is suggested to use SVR-FA for temperature forecasting.
(Less)
- author
- Aghelpour, Pouya
; Mohammadi, Babak
LU
and Biazar, Seyed Mostafa
- publishing date
- 2019-11-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Average monthly temperature, Firefly optimization algorithm, Iran, SARIMA, SVR
- in
- Theoretical and Applied Climatology
- volume
- 138
- issue
- 3-4
- pages
- 10 pages
- publisher
- Springer
- external identifiers
-
- scopus:85067255553
- ISSN
- 0177-798X
- DOI
- 10.1007/s00704-019-02905-w
- language
- English
- LU publication?
- no
- id
- 83cdc974-2452-4a3d-a711-61417f8b8f00
- date added to LUP
- 2020-12-30 05:21:40
- date last changed
- 2022-04-26 22:52:50
@article{83cdc974-2452-4a3d-a711-61417f8b8f00,
abstract = {{<p>Temporal changes of the global surface temperature have been used as a prominent indicator of global climate change; therefore, making dependable forecasts underlies the foundation of sound environmental policies. In this research, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with Firefly optimization algorithm (SVR-FA) as a meta-innovative model, in long-term forecasting of average monthly temperature. For this, 5 stations from different climates of Iran (according to the Extended De Martonne method) were selected, including Abadan, Anzali, Isfahan, Mashhad, and Tabriz. The data were collected during 1951–2011, for training (75%) and testing (25%). After selecting the best models, the average monthly temperature has been forecasted for the period 2012–2017. The results showed that the models had better performances in Extra-Arid and Warm (Abadan) and after that Extra-Arid and Cold (Isfahan) climate, in long-term forecasting. The weakest performances of the models were reported in Semi-Arid and Cold climate, including Mashhad and Tabriz. Also, despite the use of the non-linear SVR model and its meta-innovative type, SVR-FA, the results showed that, in the climates of Iran, the linear and classical SARIMA model still offers a more appropriate performance in temperature long-term forecasting. So that it could forecast the average monthly temperature of Abadan with root mean square error (RMSE) = 1.027 °C, and Isfahan with RMSE = 1.197 °C for the 6 years ahead. The SVR and SVR-FA models also had good performances. The results of this checking also report the effectiveness of the merging SVR model with the Firefly optimization algorithm in temperature forecasting in Iran’s climates, so, compared with the SVR model, it is suggested to use SVR-FA for temperature forecasting.</p>}},
author = {{Aghelpour, Pouya and Mohammadi, Babak and Biazar, Seyed Mostafa}},
issn = {{0177-798X}},
keywords = {{Average monthly temperature; Firefly optimization algorithm; Iran; SARIMA; SVR}},
language = {{eng}},
month = {{11}},
number = {{3-4}},
pages = {{1471--1480}},
publisher = {{Springer}},
series = {{Theoretical and Applied Climatology}},
title = {{Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA}},
url = {{http://dx.doi.org/10.1007/s00704-019-02905-w}},
doi = {{10.1007/s00704-019-02905-w}},
volume = {{138}},
year = {{2019}},
}