مدل ترکیبی شبکه عصبی مصنوعی- زمین آمار برای پیش‌بینی مصرف آب شهری: مطالعه موردی: شهر اسکو

نوع مقاله: مقاله پژوهشی

نویسنده

استادیار، گروه مهندسی عمران، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران

چکیده

پیش‌بینی میزان مصرف در مدیریت منابع آب، به‌ویژه در مناطق خشک و نیمه‌خشک مانند کشور ایران اهمیت بسیار زیادی دارد و برنامه‌ریزی مناسب به‌منظور بهره‌برداری مطمئن از این منابع مستلزم وجود ابزار توانمند پیش‌بینی در این زمینه است. در این پژوهش با توجه به توانایی شبکه‌های عصبی مصنوعی در مدل‌سازی سیستم‌های پیچیده و قابلیت علم زمین آمار در مدل‌سازی داده‌های مکانی، یک مدل تجربی ترکیبی به‌منظور پیش‌بینی زمانی و مکانی مصرف آب شهری تهیه و ارائه شده است. از داده‌های ایستگاه هواشناسی سهند و مصارف آب به‌دست آمده از شرکت آب و فاضلاب استان آذربایجان شرقی به منظور آموزش، صحت‌سنجی و ارزیابی نتایج مدل پیشنهادی استفاده شد. نتایج به‌دست آمده از به‌کارگیری مدل‌ ترکیبی، نشان‌دهنده قابلیت بسیار زیاد مدل‌ تهیه شده در پیش‌بینی مصرف آب شهری در محدوده مورد مطالعه است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Hybrid Artificial Neural Network-Geostatistics Model for Urban Water Consumption Prediction. A Case Study: Osku City

نویسنده [English]

  • Reza Goli Ejlali
Assist. Prof., Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
چکیده [English]

The prediction of water consumption in urban basins is of immense importance for the management of water resources, especially in arid and semiarid countries. The lack of strong predictive tools, or perhaps the lack of experienced users to those tools, may contribute to problems in data interpretation and failure to reach consensus about the need for key water management actions. Therefore, it is extremely important to comprehend the spatiotemporal variations of the water demand for the management of water in such urban areas. In this paper, a hybrid, artificial neural network – geostatistics, model is presented for spatiotemporal prediction of water consumptions. The proposed model contains two individual stages. In the first stage, an artificial neural network is trained for each station for time series modeling of water demands, so that the model can predict the water demands in the next month. At the second stage, the predicted values of water demands at different stations are imposed to a calibrated geostatistics model in order to estimate water demands at any desired point in the city. This methodology is applied for the Osku city, in East Azerbaijan Province, Iran. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and available data series. The results suggested that the hybrid model is a good choice for predicting water demands in the study area.

کلیدواژه‌ها [English]

  • prediction
  • Water Consumption
  • Hybrid Model
  • Artificial Neural Network
  • Geostatistic
  • Osku City
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