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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2019 / Cilt: 8 - Sayı: 1

USABILITY OF ARTIFICIAL INTELLIGENCE METHODS FOR ESTIMATION OF MONTHLY EVAPORATION

AYLIK BUHARLAŞMA TAHMİNİNDE YAPAY ZEKA YÖNTEMLERİNİN KULLANILABİLİRLİĞİ

Ayşe ÖZEL
Meral BÜYÜKYILDIZ
Pages
244–254
DOI
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AI Summary & Key Takeaways

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

As a result, SVR has been more successful than other methods in monthly evaporation prediction.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Evaporation, one of the most important components of the hydrological cycle, is influenced by many factors.
📊
Key Findings & Data:Evaporation is a meteorological parameter that is difficult to predict due to this complex structure.
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Conclusion & Impact:As a result, SVR has been more successful than other methods in monthly evaporation prediction.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Buharlaşma · yapay sinir ağları · destek vektör regresyonu

Abstract

Evaporation, one of the most important components of the hydrological cycle, is influenced by many factors. Evaporation is a meteorological parameter that is difficult to predict due to this complex structure. In this study, the amount of monthly evaporation was estimated using different input combinations formed by meteorological parameters belonging to Karaman station in Konya Closed Basin. For this purpose, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Adaptive Network Based Fuzzy Inference System (ANFIS) methods are used. As a result, SVR has been more successful than other methods in monthly evaporation prediction.

Özet

Hidrolojik çevrimin en önemli bileşenlerinden biri olan buharlaşma pek çok faktörün etkisindedir. Buharlaşma; bu kompleks yapısından dolayı tahmin edilmesi zor bir meteorolojik parametredir. Bu çalışmada, Konya Kapalı Havzası’nda yer alan Karaman istasyonuna ait meteorolojik parametreler kullanılarak oluşturulan farklı giriş kombinasyonları ile aylık buharlaşma miktarı tahmin edilmiştir. Bu amaçla Yapay Sinir Ağları (YSA), Destek Vektör Regresyonu (DVR), Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFIS) metotları kullanılmıştır. Sonuç olarak DVR, aylık buharlaşma tahmininde diğer metotlardan daha başarılı olmuştur.

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Cite This Article

ÖZEL, A., BÜYÜKYILDIZ, M. (2019). USABILITY OF ARTIFICIAL INTELLIGENCE METHODS FOR ESTIMATION OF MONTHLY EVAPORATION. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 8 - Sayı: 1, 244–254. https://ajindex.com/articles/289591

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