پیش‌بینی میان مدت تقاضای آب شهری با استفاده از شبکه‌های عصبی مبتنی بر الگوریتم‌های تکاملی (مطالعه موردی: شهرستان صوفیان)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه شهید مدنی آذربایجان، تبریز، ایران

2 استاد گروه مهندسی آب، دانشکده مهندسی عمران، دانشگاه تبریز

3 دانشجوی دکتری سازه‌های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه تبریز

چکیده

پیش­بینی تقاضای آب در سیستم­های آب­رسانی به منظور مدیریت صحیح منابع آب و توزیع آن امری ضروری است. با توجه به روند پرنوسان و غیرخطی مصرف آب و متغیرهای موثر بر آن، استفاده از مدل­های غیرخطی مانند شبکه­های عصبی در این زمینه توفیق بیش­تری داشته­اند. از طرفی این مدل­ها دارای نقاط ضعفی مانند نیاز به داده­های آموزشی فراوان و ضعف در یافتن نقاط بهینه سراسری می­باشند. در این مطالعه با ادغام شبکه عصبی چند لایه با الگوریتم­های تکاملی PSO و ICA، علاوه بر رفع نقایص مذکور، اقدام به آموزش شبکه و پیش­بینی روزانه مصرف آب در شهرستان صوفیان بر اساس پارامترهای هواشناسی شده است. مقایسه نتایج شبکه ترکیب شده با الگوریتم­های PSO و ICA با شبکه­ای که توسط الگوریتم کلاسیک LM آموزش دیده، نشان می­دهد که شبکه­های ترکیبی عملکرد بهتری داشته و در این بین، شبکه عصبی ترکیبی با PSO، با ضریب همبستگی 98/0 در هر یک از فصول گرم و سرد سال، دقت بالاتری نسبت به سایر شبکه­ها دارد. همچنین پیش­بینی تقاضای آب با استفاده از مدل ترکیبی طراحی شده، با چشم­انداز 10 ساله، نشان می­دهد که تقاضای آب در این شهرستان در سال 1404 حدود 40% افزایش خواهد یافت.

کلیدواژه‌ها


عنوان مقاله [English]

Forecasting Daily Urban Water Demand Using Artificial Neural Networks Based on Evolutionary Algorithms, A Case Study of Soufiyan Urban Water

نویسندگان [English]

  • nazila kardan 1
  • Yousef Hassanzadeh 2
  • Hamed Razavi Nejad 3
1 Department of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
2 Professor, Faculty of Civil Engineering, University of Tabriz
3 PhD Student of Hydraulic Structure, Faculty of Civil Engineering, University of Tabriz
چکیده [English]

Forecasting of water demand in water supply systems is essential to water resources management and its distribution as properly. According to non-linear and oscillation process of water consumption and its affecting variables, the use of non-linear models such as neural networks have get more success in this field. On the other hand, these models have some defects such as the need to more training data and weakness in finding global optimal solutions. In this study by combining the multi-layer neural networks with PSO and ICA evolutionary algorithms, the mentioned defects eliminated firstly, and then the neural networks trained and the daily water demand of Soufiyan city is predicted based on weather parameters. The results show that the hybrid neural network with PSO and ICA algorithms had better performance compared to a network that trained by LM classical algorithm. The hybrid model of neural network with PSO algorithm has correlation coefficient equal to 0.98 which have the more accurate solutions than other models in any of the warm and cold seasons. Also water demand forecasting with proposed hybrid model in the next 10 years revealed that water demand will be increased about 40% in this city.

کلیدواژه‌ها [English]

  • Forecasting
  • Imperialist Competitive Algorithm
  • Particle Swarm Optimization
  • Neural Network
  • Water Demand
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