ارزیابی مقایسه‌ای مدل عددی و شبکه عصبی مصنوعی در شبیه‌سازی کمی و کیفی آبخوان دشت نجف‌آباد

نویسندگان

1 دانشجوی دکترای مدیریت منابع آب، دانشکده مهندسی عمران و معماری، دانشگاه شهید چمران هواز

2 استاد مدیریت منابع آب، دانشکده مهندسی عمران و معماری، دانشگاه شهید چمران اهواز

3 استاد مدیریت منابع آب، دانشکده مهندسی عمران، دانشگاه صنعتی اصفهان

چکیده

هدف از انجام این مطالعه، بررسی کارایی مدل شبکه عصبی (ANN) در مدل‌سازی کمی و کیفی منابع آب زیرزمینی می‌باشد. بدین منظور، با استفاده از گزینه‌‌های MODFLOW وMT3DMS نرم افزار GMS v.10، از نظر کمی و کیفی، آب موجود در آبخوان دشت نجف آباد، واقع در حوضه آبریز گاوخونی در فلات مرکزی ایران، مدلسازی شد. بعد از واسنجی و صحت‌سنجی مدل در یک دوره مطالعه 11 ساله، محدوده تغییر ضریب هدایت هیدرولیکی بین 5/0 تا 16 (متر در روز)، آبدهی ویژه بین 023/0 تا 113/0 و ضریب پخشیدگی طولی بین 5/7 تا 2/18 (متر) بدست آمد. سپس، منطقه مورد مطالعه به دو ناحیه مجزا تقسیم و برای هر کدام یک مدل شبکه عصبی، طراحی شد. در ادامه، با استفاده از آمار 20 سال آبی و به کمک بهینه‌ساز الگوریتم ژنتیک، پارامتر‌های بهینه شبکه عصبی تعیین گردید. نهایتا، مقادیر مشاهده‌ای تراز متوسط سطح ایستابی و میانگین غلظت TDS با مقادیر محاسباتی توسط مدل عددی و شبکه عصبی، برای دو سال آبی متوالی 94-1393 و 95-1394، با یکدیگر مقایسه شدند. نتایج نشان داد که شبکه عصبی به خوبی قادر است رفتار کمی و کیفی سیستم آب زیرزمینی را شبیه‌سازی کند و می‌تواند به عنوان جایگزین مناسبی برای مدل عددی در اتصال به مدل‌های مدیریتی، استفاده شود.

کلیدواژه‌ها


عنوان مقاله [English]

Comparative Evaluation of Numerical Model and Artificial Neural Network for Quantity and Quality Simulation of Najafabad Aquifer

نویسندگان [English]

  • Masume zare 1
  • Hamid Reza Ghafouri 2
  • Hamid Reza Safavi 3
1 PhD Student of Water Resource Management, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Professor of Water Resource Management, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 Prof. of Water Resource Management, Dept. of Civil Engineering, Isfahan University of Technology, Isfahan, Iran
چکیده [English]

The aim of this study was to investigate the efficiency of the neural network model in quantitative and qualitative modeling of groundwater resources. For this purpose, the groundwater of the Najafabad aquifer located in Gavkhoni basin at the central plateau of Iran, was modeled using MODFLOW and MT3DMS modules of GMS v. 10 software. After calibrating and validating the model for a 11 years time period, the ranges of hydraulic conductivity, specific yield and longitudinal dispersivity coefficient were found to be 0.5-16 (m day-1), 0.023-0.113 and 7.5-18.2 (m), respectively. Then the study area divided into two sub-regions and the ANN model was designed for each of the sub-regions. Afterwards, the optimal parameters of the ANN models were determined using the 20-year dataset of water year and the genetic algorithm optimization model. Finally, calculated values relevant to the average level of groundwater and the mean concentration of TDS, which were acquired by the ANN model and the numerical model, were compared with the observed values from 2014 to 2016. Results showed that the neural network model is capable in simulating the quantitative and qualitative treatment of the groundwater system and can be used as a suitable alternative for the numerical model linking the management models.

کلیدواژه‌ها [English]

  • Artificial neural network
  • genetic algorithm
  • MODFLOW
  • MT3DMS
  • Simulation
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