Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

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 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Asamoah-Boaheng M (2014) Using SARIMA to forecast monthly mean surface air temperature in the Ashanti Region of Ghana. Int J Stat Appl 4(6):292–298

    Google Scholar 

  • Ashrafzadeh A, Malik A, Jothiprakash V, Ghorbani MA, Biazar SM (2018) Estimation of daily pan evaporation using neural networks and meta-heuristic approaches. ISH J Hydraul Eng:1–9

  • Bassam A, May Tzuc O, Escalante Soberanis M, Ricalde LJ, Cruz B (2017) Temperature estimation for photovoltaic array using an adaptive neuro fuzzy inference system. Sustainability 9(8):1399

    Article  Google Scholar 

  • Box GE, Jenkins GM (1976) Time series analysis: forecasting and control revised ed. Holden-Day

  • Deo RC, Ghorbani MA, Samadianfard S, Maraseni T, Bilgili M, Biazar M (2018) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Energy 116:309–323

    Article  Google Scholar 

  • Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Netw 19(2):135–144

    Article  Google Scholar 

  • Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15(3):208–216

    Article  Google Scholar 

  • EL HOUARI MB, ZEGAOUI O, ABDALLAOUI A (2015) Prediction of air temperature using multi-layer perceptrons with Levenberg-Marquardt training algorithm. Parameters 1:11

    Google Scholar 

  • Ghorbani MA, Shamshirband S, Haghi DZ, Azani A, Bonakdari H, Ebtehaj I (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 172:32–38

    Article  Google Scholar 

  • Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH, Mohammadi B (2018) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor Appl Climatol 133(3-4):1119–1131

    Article  Google Scholar 

  • Goswami K, Hazarika J, Patowary AN (2017) Monthly temperature prediction based on ARIMA model: a case study in Dibrugarh station of Assam. Ind Int J Adv Res Comp Sci 8(8)

  • Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A (2014) Modeling of daily pan evaporation in subtropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Syst Appl 41(11):5267–5276

    Article  Google Scholar 

  • Hayati M, Mohebi Z (2007) Temperature forecasting based on neural network approach. World Appl Sci J 2(6):613–620

    Google Scholar 

  • Isazadeh M, Biazar SM, Ashrafzadeh A (2017) Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters. Environ Earth Sci 76(17):610

    Article  Google Scholar 

  • Jha SK, Hayashi K (2014) A novel odor filtering and sensing system combined with regression analysis for chemical vapor quantification. Sensors Actuators B Chem 200:269–287

    Article  Google Scholar 

  • John C, George U, Chukwuemeka OS (2014) Time series analysis and forecasting of monthly maximum temperatures in south eastern Nigeria. Int J Innov Res Dev 3(1)

  • Karevan Z, Feng Y, Suykens JA (2016, April) Moving least squares support vector machines for weather temperature prediction. In: Proceedings of the European Symposium on Artificial Neural Networks, Brugge, pp 27–29

  • Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056

    Article  Google Scholar 

  • Mehr AD, Nourani V, Khosrowshahi VK, Ghorbani MA (2019) A hybrid support vector regression–firefly model for monthly rainfall forecasting. Int J Environ Sci Technol 16(1):335–346

    Article  Google Scholar 

  • Mills TC (2014) Time series modelling of temperatures: an example from Kefalonia. Meteorol Appl 21(3):578–584

    Article  Google Scholar 

  • Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597

    Google Scholar 

  • Mohammadi K, Shamshirband S, Danesh AS, Zamani M, Sudheer C (2015) Horizontal global solar radiation estimation using hybrid SVM-firefly and SVM-wavelet algorithms: a case study. Nat Hazards:1–18

  • Musa Y (2014) Modeling an average monthly temperature of Sokoto Metropolis using short term memory models. Int J Acad Res Bus Soc Sci 4(7):382

    Google Scholar 

  • Naganna SR, Deka PC, Ghorbani MA, Biazar SM, Al-Ansari N, Yaseen ZM (2019) Dew point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water 11(4):742

    Article  Google Scholar 

  • Pai PF, Hong WC (2007) A recurrent support vector regression model in rainfall forecasting. Hydrol Proc: Int J 21(6):819–827

    Article  Google Scholar 

  • Pérez-Vega A, Travieso CM, Hernández-Travieso JG, Alonso JB, Dutta MK, Singh A (2016, April) Forecast of temperature using support vector machines. In: Computing, communication and automation (ICCCA), 2016 International conference on. IEEE, pp 388–392

  • Radhika Y, Shashi M (2009) Atmospheric temperature prediction using support vector machines. Int J Comp Theor Eng 1(1):55

    Article  Google Scholar 

  • Rahimi J, Ebrahimpour M, Khalili A (2013) Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theor Appl Climatol 112(3-4):409–418

    Article  Google Scholar 

  • Romilly P (2005) Time series modelling of global mean temperature for managerial decision-making. J Environ Manag 76(1):61–70

    Article  Google Scholar 

  • Salas JD, Delleur JR, Yevjevich V, Lane WL (1988) Applied modeling of hydrologic time series. Water Resor. Pub, Littleton

    Google Scholar 

  • Shereef IK, Baboo SS (2011) A new weather forecasting technique using back propagation neural network with modified Levenberg Marquardt algorithm for learning. Int J Comp Sci Issues (IJCSI) 8(6):153

    Google Scholar 

  • Shi XIAOYU, Huang QIANG, Chang JIANXIA, Wang YIMIN, Lei J, Zhao J (2015) Optimal parameters of the SVM for temperature prediction. Proc Int Assoc Hydrol Sci 368:162–167

    Google Scholar 

  • Suzuki Y, Kaneda Y, Mineno H (2015) Analysis of support vector regression model for micrometeorological data prediction. Comp Sci Inform\ Technol 3(2):37–48

    Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183-7192

    Article  Google Scholar 

  • Vapnik, V. N., & Chervonenkis, A. J. (1974). Theory of pattern recognition.

    Google Scholar 

  • Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, Berlin, Heidelberg, pp 169–178

    Google Scholar 

  • Yang, X. S., & He, X. (2013). Firefly algorithm: recent advances and applications. arXiv preprint arXiv:1308.3898.

    Google Scholar 

  • Ye L, Yang G, Van Ranst E, Tang H (2013) Time-series modeling and prediction of global monthly absolute temperature for environmental decision making. Adv Atmos Sci 30(2):382–396

    Article  Google Scholar 

  • Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1-2):128–138

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

    Pouya Aghelpour

  2. College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China

    Babak Mohammadi

  3. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Seyed Mostafa Biazar

Authors
  1. Pouya Aghelpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Babak Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyed Mostafa Biazar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pouya Aghelpour.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aghelpour, P., Mohammadi, B. & Biazar, S.M. Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theor Appl Climatol 138, 1471–1480 (2019). https://doi.org/10.1007/s00704-019-02905-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-019-02905-w

Keywords

Search

Navigation