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Journals / Mühendislik Bilimleri ve Tasarım Dergisi / 2026 / Cilt: 14 Sayı: 2

RAINFALL PREDICTION USING THE TEMPORAL FUSION TRANSFORMER (TFT) METHOD

ZAMANSAL FÜZYON DÖNÜŞTÜRÜCÜ (TFT) YÖNTEMİYLE YAĞIŞ TAHMİNİ

Serkan Ansay(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0002-3368-3886
Bayram Köse(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0003-0256-5921
Pages
512–543
Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The obtained results demonstrate that the performance of the TFT model (RMSE = 0.039, MAE = 0.028, R² = 0.981) is significantly higher in terms of prediction accuracy and generalization capability compared to the LSTM model (RMSE = 0.061, MAE = 0.045, R² = 0.954).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Atmospheric events from the past to the present have been the subject of research to enable predictions in the environment in which they occur.
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Key Findings & Data:The obtained results demonstrate that the performance of the TFT model (RMSE = 0.039, MAE = 0.028, R² = 0.981) is significantly higher in terms of prediction accuracy and generalization capability compared to the LSTM model (RMSE = 0.061, MAE = 0.045, R² = 0.954).
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Conclusion & Impact:This superiority arises from the ability of the TFT architecture to effectively capture temporal dependencies among multivariate meteorological variables through its gated residual network structure and multi-head attention mechanism, resulting in approximately a 35% reduction in forecasting error for short-term precipitation predictions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:TFT · LSTM · Yapay Sinir Ağları

Abstract

Atmospheric events from the past to the present have been the subject of research to enable predictions in the environment in which they occur. These predictions are made by processing collected data through numerical formulas and evaluating the results. The rapid development of technology in software and hardware forms the basis for the introduction of new methods and techniques into the literature. Artificial Neural Networks, used in the prediction phase, are one of these methods. This study compares the Long Short-Term Memory (LSTM) and Temporal Fusion Transformer (TFT) methods using past total global solar radiation, sunshine duration, wind speed, cloud cover, humidity, and temperature parameters. The obtained results demonstrate that the performance of the TFT model (RMSE = 0.039, MAE = 0.028, R² = 0.981) is significantly higher in terms of prediction accuracy and generalization capability compared to the LSTM model (RMSE = 0.061, MAE = 0.045, R² = 0.954). This superiority arises from the ability of the TFT architecture to effectively capture temporal dependencies among multivariate meteorological variables through its gated residual network structure and multi-head attention mechanism, resulting in approximately a 35% reduction in forecasting error for short-term precipitation predictions.

Özet

Geçmişten günümüze atmosferde gerçekleşen olaylar olayın gerçekleştiği çevrede tahmin yürütebilmek için araştırma konusu olmaktadır. Bu tahminler, toplanan verilerin sayısal formüllerden geçirilmesiyle oluşan sonuçların değerlendirilmesiyle gerçekleşmektedir. Teknolojinin yazılım ve donanım olarak hızla gelişmesi, literatüre yeni yöntem ve tekniklerin kazandırılmasının temelini oluşturmaktadır. Tahmin aşamasında kullanılan Yapay Sinir Ağları bu yöntemlerden biridir. Bu çalışmada geçmiş toplam küresel güneş radyasyonu, güneşlenme süresi ,rüzgar hızı, bulutluluk miktarı, nem, sıcaklık parametreleri kullanılarak Uzun Kısa Süreli Bellek (LSTM- Long Short-Term Memory) ile Zamansal Füzyon Dönüştürücü (TFT- Temporal Fusion Transformer) yöntemleri karşılaştırılmıştır. Elde edilen sonuçlar, TFT modelinin (RMSE = 0.039, MAE = 0.028, R² = 0.981) performansının, LSTM modeline (RMSE = 0.061, MAE = 0.045, R² = 0.954) göre belirgin biçimde daha yüksek tahmin doğruluğu ve genelleme yeteneği sunduğunu ortaya koymaktadır. Bu üstünlük, TFT’nin çok değişkenli meteorolojik veriler arasındaki zamansal ilişkileri gated residual network yapısı ve çok başlı dikkat (multi-head attention) mekanizması aracılığıyla etkili biçimde yakalayabilmesinden kaynaklanmaktadır; böylece kısa vadeli yağış tahmininde yaklaşık %35 düzeyinde hata azalımı elde edilmiştir.

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Cite This Article

Ansay, S., Köse, B. (2026). RAINFALL PREDICTION USING THE TEMPORAL FUSION TRANSFORMER (TFT) METHOD. Mühendislik Bilimleri ve Tasarım Dergisi, Cilt: 14 Sayı: 2, 512–543. https://doi.org/10.21923/jesd.1787789

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