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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

Application of machine learning for future water flow rate estimations on Ipsala water metering station

İpsala su ölçüm istasyonunda gelecekteki su debisi tahminleri için makine öğrenmesinin uygulanması

Akın İlhan(ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ)0000-0003-3590-5291
Pages
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Publication Date
2026-05-20
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AI Summary & Key Takeaways

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

It has been concluded that especially fuzzy c-means of adaptive neuro fuzzy inference system approach is very accurate and effective method in future value prediction of water flow rate, which is also validated as novelty by statistical paired t-test and different discharge level scenarios.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Latterly, forecasting of flow parameters has significantly increased; one of those is estimation of water flow rate.
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Key Findings & Data:For example, when water flow estimation of a river is considered, estimation of future values of water flow rate is momentous in terms of a response to flash floods.
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Conclusion & Impact:It has been concluded that especially fuzzy c-means of adaptive neuro fuzzy inference system approach is very accurate and effective method in future value prediction of water flow rate, which is also validated as novelty by statistical paired t-test and different discharge level scenarios.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Uyarlanabilir nöro-bulanık çıkarım sistemi · Veri tahmini · Uzun kısa süreli bellek

Abstract

Latterly, forecasting of flow parameters has significantly increased; one of those is estimation of water flow rate. Prediction of unknown future value of this physical parameter is very important in terms of guiding people on some issues. For example, when water flow estimation of a river is considered, estimation of future values of water flow rate is momentous in terms of a response to flash floods. A couple of algorithms that use machine learning methods have been proposed in this study, in forecast of one-ahead observed value of water flow rate of measured experimental data belonging to Ergene river, flowing in Türkiye. Ipsala water metering station established on this river was chosen as study area. Algorithms used in the study construct a total of 93 different models. Water flow rate anticipations of Ergene river have indicated that fuzzy c-means and long short-term memory had generated superior results in terms of statistical accuracy measures. It has been concluded that especially fuzzy c-means of adaptive neuro fuzzy inference system approach is very accurate and effective method in future value prediction of water flow rate, which is also validated as novelty by statistical paired t-test and different discharge level scenarios.

Özet

Son zamanlarda, akış parametrelerinin tahmin edilmesi önemli ölçüde artmıştır; bunlardan birisi de su akış hızının tahminidir. Bu fiziksel parametrenin bilinmeyen gelecekteki değerinin tahmini, bazı konularda, insanlara yol göstermesi açısından çok önemlidir. Örneğin, bir nehrin su akış tahmini söz konusu olduğunda, ani sellere karşı müdahale açısından, su akış hızının gelecekteki değerlerinin tahmini çok hayatidir. Bu çalışmada, Türkiye'de akan Ergene Nehri'ne ait ölçülen deneysel verilerin bir sonraki gözlemlenen su akış hızı değerinin tahmininde makine öğrenmesi yöntemlerini kullanan birkaç algoritma önerilmiştir. Çalışma alanı olarak bu nehir üzerinde kurulu Ipsala su ölçüm istasyonu seçilmiştir. Çalışmada kullanılan algoritmalar; toplamda 93 farklı model oluşturmuştur. Ergene Nehri'nin su akış hızı tahminleri, bulanık c-ortalamalar ve uzun kısa süreli bellek algoritmalarının istatistiksel doğruluk ölçütleri açısından üstün sonuçlar ürettiğini göstermiştir. Özellikle; uyarlanabilir nöro bulanık çıkarım sistemine ait bulanık c-ortalamaları yaklaşımının, su akış hızının gelecekteki değer tahmininde çok doğru ve etkili bir yöntem olduğu sonucuna varılmıştır; bu yöntem, istatistiksel eşleştirilmiş t-testi ve farklı tahliye seviyesi senaryoları ile de yenilik olarak doğrulanmıştır.

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Cite This Article

İlhan, A. (2026). Application of machine learning for future water flow rate estimations on Ipsala water metering station. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1853973

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