روش نوین نشت‌یابی با استفاده از شبکه‌های عصبی مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای مهندسی آب و سازه‌های هیدرولیکی، دانشگاه فردوسی مشهد، ایران

2 استاد گروه عمران، دانشگاه فردوسی مشهد، ایران

چکیده

در حال حاضر هدر رفت آب به یک نگرانی جهانی تبدیل شده است. تقاضا برای آب در حال افزایش است. این مسئله مدیریت تقاضا و اصلاح الگوی مصرف را ضروری ساخته است. از مهم‌ترین روش‌های مدیریت مصرف، کاهش آب به حساب نیامده است. در این پژوهش یک ایده جدید برای تعیین موقعیت و مقدار نشت‌های موجود در شبکه‌های توزیع آب با استفاده از شبکه‌های عصبی معرفی شد. در این روش با تولید داده‌های آموزشی و اعمال آن به شبکه عصبی، شبکه قادر خواهد بود که با دریافت فشار گرهی، موقعیت و مقدار تقریبی نشت گرهی را تعیین کند. تولید داده‌های آموزشی با اعمال نشت فرضی در گره‌های مشخصی از شبکه و برداشت فشار گرهی انجام می‌شود. نتایج نشان می‌دهد که این روش می‌تواند با حداقل برداشت اطلاعات هیدرولیکی از نوع فشارها، علاوه بر تعیین موقعیت نشت‌های موجود گرهی، میزان نشت در هر یک از گره‌ها را نیز با دقت مناسبی تعیین کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

New Method for Leakage Detection by Using Artificial Neural Networks

نویسندگان [English]

  • Mohammad Attari 1
  • Mahmoud Faghfour Maghrebi 2
1 PhD Student of Water Engineering & Hydraulic Structues, Ferdowsi University, Mashhad, Iran
2 Prof., Department of Civil Engineering, Ferdowsi University, Mashhad, Iran
چکیده [English]

Nowadays water loss has been turned into a global concern and on the other hand the demand for water is increasing. This problem has made the demand management and consumption pattern reform necessary. One of the most important methods for managing water consumption is to decrease the water loss. In this study by using neural networks, a new method is presented to specify the location and quantity of leakages in water distribution networks.  In this method, by producing the training data and applying it to neural network, the network is able to determine approximate location and quantity of nodal leakage with receiving the nodal pressure. Production of training data is carried out by applying assumed leakage to specific nodes in the network and calculating the new nodal pressures. The results show that by minimum use of hydraulic data taken from pressures, not only this method can determine the location of nodal leakages, but also it can specify the amount of leakage on each node with reasonable accuracy.

کلیدواژه‌ها [English]

  • Leakage Detection
  • Pressure Measurement
  • Neural Network
  • Water Distribution Network
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