Afyonkarahisar’da İklim Değişikliği ve Sürdürülebilir Enerji Stratejileri: Sıcaklık Tahmin Yaklaşımı

Yıl 2025, Cilt: 15 Sayı: 4, 1447 - 1471, 15.12.2025

Feyza Nur Yeşil , Tuba Nur Serttaş

https://doi.org/10.31466/kfbd.1600290

Öz

Küresel sıcaklıkların artmasına neden olan iklim değişikliği, özellikle kuraklık açısından Türkiye için ciddi bir tehdit oluşturmaktadır. Artan sıcaklıklar yalnızca insan sağlığını tehlikeye atmakla kalmaz; aynı zamanda bulaşıcı hastalıkların yayılmasını hızlandırır, ekolojik döngüleri bozar, düzensiz yağış modelleri oluşturur, tarımsal verimliliği azaltır ve kaynak kıtlığını daha da kötüleştirir. Bu nedenle, sıcaklık eğilimlerinin izlenmesi; tarımsal alanların iyileştirilmesi, su kaynaklarının korunması, sürdürülebilir enerji girişimlerinin uygulanması ve etkili iklim eylem planlarının oluşturulması için önemlidir. Bu bağlamda, mevcut çalışma tarım ve yenilenebilir enerji açısından stratejik öneme sahip bir bölge olan Afyonkarahisar için sıcaklık tahminine odaklanmaktadır. Afyonkarahisar Meteoroloji Müdürlüğü'nden alınan 2018-2022 yılları arasındaki saatlik sıcaklık verileri, Box-Jenkins yöntemlerine dayalı ARIMA ve SARIMA modellerini uygulamak için kullanılmıştır. Bu modellerin tahmin yeteneklerini göstermek için temel bir kıyaslama olarak Mevsimsel Naif Tahmin modeli kullanıldı. Performansları, son yıl için çeyreklik dönemler üzerinden performans ölçütleri kullanılarak karşılaştırmalı olarak analiz edildi. Geliştirilen ARIMA(2,1,1) modeli, SARIMA(2,1,1)(1,1,2)₁₂ modelini geride bırakarak RMSE'de %11,06, MAE'de %10,80 ve R²'de %10,92 iyileştirme elde etti; ayrıca, RMSE'de %60,59 ve MAE'de %61,89 iyileştirmelerle Mevsimsel Naif Tahmin modelini geçti. Deneysel sonuçlar, ARIMA modelinin mevsimsel sıcaklık eğilimlerini ve değişimlerini etkili bir şekilde yakalayarak maliyet açısından uygun, uzun vadeli doğru tahminler sağladığını göstermektedir.

Anahtar Kelimeler

Hava sıcaklık tahmini , Box-Jenkins , ARIMA , SARIMA , İklim değişikliği

Climate Change and Sustainable Energy Strategies in Afyonkarahisar: A Temperature Forecasting Approach

Öz

Climate change, which results in rising global temperatures, poses a significant threat to Turkey, particularly regarding drought. Increasing temperatures not only jeopardize human health but also facilitate the spread of infectious diseases, disrupt ecological cycles, create irregular precipitation patterns, diminish agricultural productivity, and worsen resource scarcity. Consequently, monitoring temperature trends is essential for enhancing agricultural lands, conserving water resources, implementing sustainable energy initiatives, and formulating effective climate action plans. In this context, the present study focuses on temperature forecasting for Afyonkarahisar, a region of strategic importance for agriculture and renewable energy. Hourly temperature data from 2018 to 2022, obtained from the Afyonkarahisar Meteorological Service, were utilized to implement ARIMA and SARIMA models based on Box-Jenkins methods. The Seasonal Naive Forecast model was used as a basic benchmark to demonstrate the predictive capabilities of these models. Their performance was comparatively analyzed by using performance metrics evaluated over quarterly periods for the last year. The developed ARIMA(2,1,1) model outperformed the SARIMA(2,1,1)(1,1,2)₁₂ model, achieving improvements of 11.06% in RMSE, 10.80% in MAE, and 10.92% in R²; additionally, it surpassed the Seasonal Naive Forecast model with improvements of 60.59% in RMSE and 61.89% in MAE. The experimental results demonstrate that the ARIMA model effectively captures seasonal temperature trends and variations, providing accurate and cost-effective long-term forecasts.

Anahtar Kelimeler

Air temperature predict , Box-Jenkins , ARIMA , SARIMA , Climate changes

Kaynakça

• Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
• Alencar, D. B. de, Affonso, C. M., Limão de Oliveira, R. C., & Reston Filho, J. C. (2018). Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil. IEEE Access, 6, 55986–55994. https://doi.org/10.1109/ACCESS.2018.2872720
• Alomar, M. K., Khaleel, F., Aljumaily, M. M., Masood, A., Razali, S. F. M., AlSaadi, M. A., ... & Hameed, M. M. (2022). Data-driven models for atmospheric air temperature forecasting at a continental climate region. PLoS One, 17(11), e0277079.
• Amjad, M., Khan, A., Fatima, K., Ajaz, O., Ali, S., & Main, K. (2022). Analysis of temperature variability, trends and prediction in the Karachi Region of Pakistan using ARIMA models. Atmosphere, 14(1), 88. https://doi.org/10.3390/atmos14010088
• Aghelpour, P., Mohammadi, B., & Biazar, S. M. (2019). Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoretical and Applied Climatology, 138(3), 1471-1480.
• Beggs, P. J. (2004). Impacts of climate change on aeroallergens: past and future. Clinical & Experimental Allergy, 34(10), 1507–1513. https://doi.org/10.1111/J.1365-2222.2004.02061.X
• Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day.
• Demirbaş, M., & Aydin, R. (2020). 21. Yüzyılın En Büyük Tehdidi: Küresel İklim Değişikliği. E-Journal of New World Sciences Academy, 15(4), 163–179. https://doi.org/10.12739/NWSA.2020.15.4.5A0143
• De Saa, E., & Ranathunga, L. (2020). Comparison between arima and deep learning models for temperature forecasting. https://doi.org/10.48550/arXiv.2011.04452
• Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
• Dinc Cavlak, O. (2024). Sürdürülebilir hisse senedi endekslerinin DCC-GARCH modeli ile incelenmesi ve petrol fiyatlarının bu ilişkiye etkisi. Afyon Kocatepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 26(1), 48-58. https://doi.org/10.33707/akuiibfd.1335551
• Franchini, M., & Mannucci, P. M. (2015). Impact on human health of climate changes. European Journal of Internal Medicine, 26(1), 1–5. https://doi.org/10.1016/J.EJIM.2014.12.008
• Fu, H.-Z. (2022). A large-scale bibliometric analysis of global climate change research between 2001 and 2018. Climatic Change, 170(3–4). https://doi.org/10.1007/s10584-022-03324-z
• Haque, E., Tabassum, S., & Hossain, E. (2021). A comparative analysis of deep neural networks for hourly temperature forecasting. IEEE Access, 9, https://doi.org/10.1109/ACCESS.2021.3131533
• Hamilton, J. D. (2020). Time series analysis. Princeton university press.
• Hou, J., Wang, Y., Zhou, J., & Tian, Q. (2022). Prediction of hourly air temperature based on CNN–LSTM. Geomatics, Natural Hazards and Risk, 13(1), 1962-1986. https://doi.org/10.1080/19475705.2022.2102942
• Huang, Y., Zhao, H., & Huang, X. (2019, February). A prediction scheme for daily maximum and minimum temperature forecasts using recurrent neural network and rough set. In IOP Conference Series: Earth and Environmental Science (Vol. 237, No. 2, p. 022005). IOP Publishing. https://doi.org/10.1088/1755-1315/237/2/022005
• Islam, M., & Zakaria, M. T. (2019). Forecasting of maximum and minimum temperature in the Cox’s Bazar Region of Bangladesh based on time series analysis. IOSR J. Math. IOSR-JM, 15, 56-67. https://doi.org/10.9790/5728-1505035667
• Joanes, D.N. and Gill, C.A. (1998), Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician), 47: 183-189. https://doi.org/10.1111/1467-9884.00122
• Karabulut, M. A., & Topçu, E. (2022). Deri̇n öğrenme tekni̇ği̇ kullanilarak kars i̇li̇ni̇n hava sicaklik tahmi̇ni̇. Mühendislik Bilimleri ve Tasarım Dergisi, 10(4), 1174–1181. https://doi.org/10.21923/jesd.1067700
• Kisi, O., & Shiri, J. (2014). Prediction of long‐term monthly air temperature using geographical inputs. International Journal of Climatology, 34(1), 179–186. https://doi.org/10.1002/JOC.3676
• Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of econometrics, 54(1-3), 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
• Lai, Y., & Dzombak, D. A. (2020). Use of the autoregressive integrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather and forecasting, 35(3), 959-976. https://doi.org/10.1175/WAF-D-19-0158.1
• Li, G., & Yang, N. (2023). A hybrid sarima‐lstm model for air temperature forecasting. Advanced Theory and Simulations, 6(2), 2200502.
• Murat, M., Malinowska, I., Gos, M., & Krzyszczak, J. (2018). Forecasting daily meteorological time series using ARIMA and regression models. International agrophysics, 32(2). https://doi.org/10.1515/intag-2017-0007
• Naing, W. Y. N., & Htike, Z. Z. (2015). Forecasting of monthly temperature variations using random forests. ARPN journal of Engineering and Applied Sciences, 10(21), 10109-10112.
• Nketiah, E. A., Chenlong, L., Yingchuan, J., & Aram, S. A. (2023). Recurrent neural network modeling of multivariate time series and its application in temperature forecasting. Plos one, 18(5), e0285713. https://doi.org/10.1371/journal.pone.0285713
• Ozbek, A., Sekertekin, A., Bilgili, M., & Arslan, N. (2021). Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA. Arabian Journal of Geosciences, 14, 1-16. https://doi.org/10.1007/s12517-021-06982-y
• Öztürk, K. (2002). Küresel iklim değişikliği ve Türkiyeye olası etkileri. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi, 22(1).
• Park, I., Kim, H. S., Lee, J., Kim, J. H., Song, C. H., & Kim, H. K. (2019). Temperature prediction using the missing data refinement model based on a long short-term memory neural network. Atmosphere, 10(11), 718. https://doi.org/10.3390/atmos10110718
• Polat, E., & Kahraman, S. (2021). Antroposen Çağı’nda pandemi ve kentlerin durumu. 41, 21–31. https://doi.org/10.33613/ANTROPOLOJIDERGISI.810841
• Roy, D. S. (2020). Forecasting the air temperature at a weather station using deep neural networks. Procedia computer science, 178, 38-46. https://doi.org/10.1016/j.procs.2020.11.005
• Salcedo-Sanz, S., Deo, R. C., Carro-Calvo, L., & Saavedra-Moreno, B. (2016). Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoretical and Applied Climatology, 125(1), 13–25. https://doi.org/10.1007/S00704-015-1480-4
• Sanikhani, H., Deo, R. C., Samui, P., Kisi, O., Mert, C., Mirabbasi, R., ... & Yaseen, Z. M. (2018). Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Computers and Electronics in Agriculture, 152, 242-260.
• Sekertekin, A., Bilgili, M., Arslan, N., Yildirim, A., Celebi, K., & Ozbek, A. (2021). Short-term air temperature prediction by adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) network. Meteorology and Atmospheric Physics, 133, 943-959. https://doi.org/10.1007/s00703-021-00791-4
• Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461-464.
• Shen, H., Jiang, Y., Li, T., Cheng, Q., Zeng, C., & Zhang, L. (2020). Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data. Remote Sensing of Environment, 240, 111692. https://doi.org/10.1016/j.rse.2020.111692
• Singh, S., Kaushik, M., Gupta, A., & Malviya, A. K. (2019, March). Weather forecasting using machine learning techniques. In Proceedings of 2nd international conference on advanced computing and software engineering (ICACSE). http://dx.doi.org/10.2139/ssrn.3350281
• Tektaş, M. (2010). Weather Forecasting Using ANFIS and ARIMA MODELS. Environmental Research, Engineering and Management, 51(1), 5–10. https://doi.org/10.5755/J01.EREM.51.1.58
• Thi Kieu Tran, T., Lee, T., Shin, J. Y., Kim, J. S., & Kamruzzaman, M. (2020). Deep learning-based maximum temperature forecasting assisted with meta-learning for hyperparameter optimization. Atmosphere, 11(5), 487. https://doi.org/10.3390/atmos11050487
• Tuğaç, Ç. (2022). İKLİM DEĞİŞİKLİĞİ KRİZİ VE ŞEHİRLER. Çevre Şehir Ve İklim Dergisi, 1(1), 38-60.
• Turan, E. S. (2018). Türkiye’nin İklim Değişikliğine Bağlı Kuraklık Durumu. Dogal Afetler ve Cevre Dergisi, 4(1), 63–69. https://doi.org/10.21324/DACD.357384
• Türkeş, M. (1997). Hava ve iklim kavramları üzerine. TÜBİTAK Bilim ve Teknik Dergisi, 355, 36-37.
• Türkeş, M. (2000). Hava, İklim, Şiddetli Hava Olayları ve Küresel Isınma. TC Başbakanlık Devlet Meteoroloji İşleri Genel Müdürlüğü, 187, 205.
• UNFCCC. (1992). United nations framework convention on climate change.
• Zaytar, M. A., & El Amrani, C. (2016). Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. International Journal of Computer Applications, 143(11), 7-11.
• Wang, H., Huang, J., Zhou, H., Zhao, L., & Yuan, Y. (2019). An integrated variational mode decomposition and ARIMA model to forecast air temperature. Sustainability, 11(15), 4018. https://doi.org/10.3390/su11154018
• Wang, S., & Ma, J. (2023, October). A Novel Ensemble Model for Load Forecasting: Integrating Random Forest, XGBoost, and Seasonal Naive Methods. In 2023 2nd Asian Conference on Frontiers of Power and Energy (ACFPE) (pp. 114-118). IEEE.
• World Economic Forum. (2022). The global risks report.