Yapay Zeka İle Atıksu Toplama Sistemlerinde Katı Madde Birikiminin ve Tıkanıklığın Tespit Edilmesi

Öz

Atıksu toplama sistemlerinde katı madde birikimi, tıkanmalara, akış kapasitesinin azalmasına ve Ayrık veya Birleşik Kanal Taşmaları (SSO veya CSO) riskinin artmasına yol açarak sel ve patojen bulaşması gibi halk sağlığını tehdit eden ciddi sorunlara neden olmaktadır. Bu tür birikimlerin erken tespiti kritik öneme sahip olsa da mevcut otomatik yöntemler, yalnızca derinlik verilerine dayanan ve hız bilgisi eksikliği nedeniyle tutarsız sonuçlar veren akademik çalışmalarla sınırlıdır. Bu çalışmada, Yağmur Suyu Yönetim Modeli (SWMM) ile oluşturulan 1.482.000 simülasyon sonucunda derinlik-hız dağılım grafiklerinden 82.482 etiketli silt birikim seviyesi grafiği kullanılarak bir Derin Evrişimli Sinir Ağı (CNN) eğitilmiştir. CNN, test verilerinde (katı madde miktarı tahminlerinde %10’a kadar olan sapmalarla) %99 doğruluk oranına ulaşmış ve 20 cm çapındaki borularla yapılan üç gerçek deney ile doğrulanmıştır. Bu deneylerde, tıkanmalar gerçek koşullara sırasıyla 0 cm, 0,3 cm ve 1,5 cm yakınlıkta tahmin edilmiştir. Sonuçlar, CNN’in silt birikimini tespit etmede etkili bir araç olduğunu, geleneksel yöntemlere göre daha hızlı ve hassas bir çözüm sunduğunu, bakım süreçlerini iyileştirme ve sistem arıza risklerini azaltma potansiyeline sahip olduğunu göstermektedir. Ancak, modelin daha geniş kapsamlı gerçek hayattaki uygulamalar için optimize edilmesine ve ek araştırmalara ihtiyaç duyulmaktadır.

Anahtar Kelimeler

• Atıksu ve Yağmur suyu Toplama Sistemleri
• Katı Madde Birikimi
• Yapay Zeka
• Evrişimli Sinir Ağı

LEVERAGING AI TO IDENTIFY SILT ACCUMULATION AND BLOCKAGE IN COLLECTION SYSTEMS

Öz

Solid waste accumulation in wastewater collection systems poses significant challenges, leading to blockages, reduced flow capacity, and an increased risk of Separate or Combined Sewer Overflows (SSO or CSO), threatening public safety through flooding and pathogen contamination. Proactive detection of such buildups is critical, yet current automated methods are limited to academic studies that rely solely on depth data, lacking velocity information essential for determining silt depth accurately. This study addresses this gap by training a Deep Convolutional Neural Network (CNN) on depth-velocity scatter plots generated from 1,482,000 simulations using the Storm Water Management Model (SWMM), producing 82,482 labeled plots of varying silt accumulation levels. The CNN achieved 99% accuracy (for solid content predictions with deviations of up to 10%) on test data and was further validated with three real-world experiments using 20 cm pipes, where blockages were predicted within 0 cm, 0.3 cm, and 1.5 cm of actual conditions. These findings demonstrate CNNs as an effective tool for identifying silt accumulation, offering a faster and more precise alternative to traditional methods, with the potential to enhance maintenance and reduce system failure risks. Further research is needed to optimize the model for broader real-world applications.

Anahtar Kelimeler

• Sanitary Sewer and Stormwater Collection Systems
• Silt Accumulation
• Artificial Intelligent
• Convolutional Neural Network

Kaynakça

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