TY  - JOUR
T1  - LSTM ve Hibrit CNN–LSTM Derin Öğrenme Yaklaşımları ile Isparta İli İçin Zaman Serisi Tabanlı Sıcaklık Tahmini
TT  - Time Series–Based Temperature Forecasting for Isparta Province Using LSTM and Hybrid CNN–LSTM Deep Learning Approaches
AU  - Çoban, Erdem
PY  - 2025
DA  - November
Y2  - 2025
DO  - 10.71445/umbd.1761095
JF  - Uluborlu Mesleki Bilimler Dergisi
PB  - Isparta Uygulamalı Bilimler Üniversitesi
WT  - DergiPark
SN  - 2651-5423
SP  - 104
EP  - 117
VL  - 8
IS  - 3
LA  - tr
AB  - 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.
KW  - CNN
KW  - Derin öğrenme
KW  - LSTM
KW  - Sıcaklı Tahmini
KW  - XGBoost
KW  - Zaman Serisi
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UR  - https://doi.org/10.71445/umbd.1761095
L1  - https://dergipark.org.tr/tr/download/article-file/5136332
ER  -