gummfd

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

1300-1884 1304-4915

Gazi Üniversitesi

10.17341/gazimmfd.1662870

Deep Learning Neural Networks Machine Learning (Other) Spatial Data and Computing Applications

Derin Öğrenme Nöral Ağlar Makine Öğrenme (Diğer) Mekansal Veri ve Bilgi İşleme

Transformatör modelleri ile zaman serisi analizi tabanlı toprak sıcaklığı tahmini

https://orcid.org/0000-0002-4031-1765

Yurtsever

Muhammet Mücahit Enes

KOCAELİ ÜNİVERSİTESİ

https://orcid.org/0000-0003-1497-305X

Kilimci

Zeynep Hilal

KOCAELİ ÜNİVERSİTESİ

https://orcid.org/0000-0002-1886-1250

Küçükmanisa

Ayhan

KOCAELİ ÜNİVERSİTESİ

03 31 2026

41 1 579 594 03 21 2025 01 19 2026

1986

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Toprak sıcaklığı; buzulların enerji dengesi, kütle dengesi, ekolojik istikrar ve tarımsal verimlilik üzerinde belirleyici bir değişken olup, karmaşık ve doğrusal olmayan etkileşimler nedeniyle doğru biçimde tahmin edilmesi güçtür. Mevcut yöntemler bu karmaşıklıkları yeterince temsil edemediğinden, daha gelişmiş modellere ihtiyaç duyulmaktadır. Bu çalışma, toprak sıcaklığı tahmini için transformatör tabanlı yeni bir çerçeve önermektedir. Transformatör mimarilerinin uzun vadeli bağımlılıkları ve zamansal örüntüleri yakalama konusundaki üstün yeteneklerinden yararlanılarak çevresel veriler üzerinde yüksek doğruluklu bir tahmin modeli geliştirilmiştir. Modelin geliştirilmesi ve değerlendirilmesi için altı farklı FLUXNET istasyonundan elde edilen veriler kullanılmıştır. Karşılaştırmalı analizler; ağaç tabanlı yöntemler, klasik derin öğrenme mimarileri ve beş gelişmiş transformatör modeli (Vanilla Transformer, Informer, Autoformer, Reformer ve ETSformer) ile gerçekleştirilmiştir. Sonuçlar, transformatör tabanlı modellerin tahmin doğruluğu bakımından geleneksel yaklaşımlara kıyasla belirgin üstünlük sağladığını göstermektedir. Ayrıca önerilen yöntemin farklı çevresel koşullarda tutarlı, sağlam ve genelleştirilebilir performans sergilediği doğrulanmıştır. Bulgular, transformatör modellerinin çevresel tahmin problemlerinde, özellikle toprak sıcaklığı öngörüsünde, yüksek potansiyele sahip olduğunu ortaya koymakta hem bilimsel anlayışa katkı sağlamakta hem de ölçeklenebilir, güvenilir ve pratik uygulamalara uygun bir araç sunmaktadır.

Derin öğrenme FLUXNET Toprak sıcaklığı tahmini Transformatör modeller Zaman serisi kestirimi

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