LSTM ve Hibrit CNN–LSTM Derin Öğrenme Yaklaşımları ile Isparta İli İçin Zaman Serisi Tabanlı Sıcaklık Tahmini

Yıl 2025, Cilt: 8 Sayı: 3, 104 - 117, 29.11.2025

Erdem Çoban

https://doi.org/10.71445/umbd.1761095

Öz

Günlük hava sıcaklığı, tarım, enerji, sağlık ve su yönetimi gibi pek çok sektörü doğrudan etkileyen kritik bir meteorolojik parametredir. Küresel iklim değişikliğiyle birlikte sıcaklık rejimlerindeki dalgalanmalar ve uzun vadeli artışlar, bölgesel tahmin modellerinin geliştirilmesini daha da önemli hale getirmiştir. Bu çalışmada, Isparta ili için günlük hava sıcaklığı tahmini amacıyla dört farklı modelin (XGBoost, LSTM, CNN ve CNN–LSTM) karşılaştırmalı analizi gerçekleştirilmiştir. Geçmiş yıllara ait günlük sıcaklık verileri kullanılarak yapılan tahminlerde, modeller hem eğitim hem de test setleri üzerinde MAE, MSE, R², NSE ve Willmott d gibi istatistiksel ölçütlerle değerlendirilmiştir. Elde edilen sonuçlar, test verisi üzerinde LSTM modelinin en yüksek doğruluğu sunduğunu göstermiştir (MAE 1,139, MSE 2,334, R² 0,966, NSE 0,965, Willmott d 0,991). CNN–LSTM modeli ise LSTM’e çok yakın değerlerle (MAE 1,236, MSE 2,661, R² 0,962, NSE 0,960, Willmott d 0,990) istikrarlı bir alternatif olarak öne çıkmıştır. CNN modeli rekabetçi performans sergilemiş (MAE 1,228, MSE 2,688, R² 0,961, NSE 0,960, Willmott d 0,989), XGBoost modeli ise diğer modellere kıyasla daha zayıf kalmıştır (MAE 2,526, MSE 10,63, R² 0,855, NSE 0,843, Willmott d 0,960). Genel olarak, LSTM modeli uzun vadeli bağımlılıkları yakalama başarısıyla öne çıkarken, CNN–LSTM modeli kısa dönemli örüntüleri de dikkate alarak güvenilir ve kararlı bir tahmin yaklaşımı sunmuştur. Bu bulgular, derin öğrenme modellerinin yerel ölçekli sıcaklık tahminlerinde karar vericilere daha doğru öngörüler sağlayabileceğini ortaya koymaktadır.

Anahtar Kelimeler

CNN , Derin öğrenme , LSTM , Sıcaklı Tahmini , XGBoost , Zaman Serisi

Time Series–Based Temperature Forecasting for Isparta Province Using LSTM and Hybrid CNN–LSTM Deep Learning Approaches

Öz

Temperature and its variations play a crucial role in agriculture, energy consumption, and urban planning. Accurately forecasting air temperature is essential for sustainable resource management and climate adaptation strategies. In this study, four different models—XGBoost, LSTM, CNN, and the hybrid CNN–LSTM—were applied for daily temperature prediction in Isparta, a region sensitive to climatic fluctuations. The performance of the models was evaluated using statistical metrics including MAE, MSE, R², NSE, and Willmott’s d. The results show that the LSTM model achieved the highest accuracy on the test data, with a mean absolute error of 1.14 °C, mean squared error of 2.33, coefficient of determination of 0.966, Nash–Sutcliffe efficiency of 0.965, and Willmott’s d index of 0.991. The CNN–LSTM model provided very close results, with a mean absolute error of 1.23 °C, mean squared error of 2.66, coefficient of determination of 0.962, Nash–Sutcliffe efficiency of 0.960, and Willmott’s d index of 0.990, demonstrating a stable predictive ability. The CNN model also produced competitive results, while the XGBoost algorithm showed weaker performance with higher error values and lower generalization capability. These findings highlight that LSTM is most effective in capturing long-term dependencies, whereas the CNN–LSTM hybrid offers a robust alternative capable of modeling both short-term patterns and long-term dynamics. Thus, both models can be considered valuable tools for local meteorological forecasting and climate-aware decision-making.

Anahtar Kelimeler

CNN , Deep learning , LSTM , Temperature forecasting , XGBoost , Time series

Kaynakça

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