LEVERAGING AI TO IDENTIFY SILT ACCUMULATION AND BLOCKAGE IN COLLECTION SYSTEMS

Authors : Mehmet Bülent Ercan

Pages : 997-1010

Doi:10.17482/uumfd.1601353

View : 69 | Download : 146

Publication Date : 2025-12-19

Article Type : Research Paper

Abstract :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.
Keywords : Atıksu ve Yağmur suyu Toplama Sistemleri, Katı Madde Birikimi, Yapay Zeka, Evrişimli Sinir Ağı

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