استفاده از شبکۀ عصبی‌ـ فازی تطبیق‌پذیر (ANFIS) به‌منظور پیش‌بینی کیفیت آب زیر‌زمینی در غرب استان فارس طی سال‌های 1383 تا 1393

نوع مقاله : پژوهشی

نویسندگان

1 عضو هیئت علمی، دانشکدۀ کشاورزی و منابع طبیعی داراب، دانشگاه شیراز

2 دانشجوی دکتری برق قدرت، دانشگاه صنعتی شیراز

3 عضو هیئت علمی، گروه مهندسی منابع طبیعی (مرتع و آبخیزداری)، دانشکدۀ کشاورزی، دانشگاه فسا*

4 عضو هیئت علمی، گروه مهندسی برق و الکترونیک، دانشگاه صنعتی شیراز

چکیده

با توجه به کاهش بارندگی و استفادۀ بیش از حد از آب‌های زیر‌زمینی، بررسی کیفیت آنها از مهم‌ترین چالش‌های بحث‌شده در مناطق مختلف از جمله ایران است. تخمین کیفیت آب از طریق مدل‏سازی، از جمله استفاده از شبکه‏های عصبی، موجب کاهش هزینه و مدیریت بهتر می‏شود. بنابراین، تحقیق حاضر با هدف بررسی کیفیت آب زیر‏زمینی در یک دورۀ 10 ساله (1383 تا 1393) با استفاده از شبکه‏های عصبی‌ـ فازی تطبیق‏پذیر (ANFIS) در غرب استان فارس انجام گرفت. در این مطالعه از سه روش grid partitioning، clustering sub و FCM در دو حالت هیبرید و پس‌انتشار خطا به‌منظور پیش‏بینی کیفیت آب زیر‌زمینی استفاده شد. پارامترهای آموزش در این مطالعه، هدایت الکتریکی (EC) و نسبت جذب سدیم (SAR) هستند. همچنین برای آموزش شبکه از کلاس‏های کیفیت آب تهیه‌شده توسط دیاگرام ویلکاکس استفاده شد. در آلودگی شیمیایی، طبق دیاگرام ویلکاکس نسبت جذب سدیم و هدایت الکتریکی مهم‌ترین فاکتورهایی هستند که با اندازه‏گیری آنها می‌توان آب منطقۀ مطالعه‌شده را در کلاس‏های مختلف مانند خیلی مناسب، مناسب و نامناسب برای آبیاری کلاس‌بندی کرد. بر اساس نتایج از بین مدل‏های مختلف پیش‌بینی کیفیت آب زیرزمینی، مدل هیبرید در روش FCM با بیشترین R (99/0) و کمترین خطا، بیشترین دقت در پیش‌بینی کیفیت آب زیرزمینی منطقۀ مطالعه‌شده را دارد.
 
 
 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period

نویسندگان [English]

  • Marzieh Mokarram 1
  • Mohammad Jaafar Mokarram 2
  • Abdol Rassoul Zarei 3
  • Behrouz Safarinejadian 4
1 Professor, Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Iran
2 PhD Student of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
3 Professor, Department of Range and Watershed Management, College of Agriculture, University of Fasa, Iran
4 Professor, Electrical and Electronics Engineering Department, Shiraz University of Technology, Shiraz, Iran
چکیده [English]

Due to the reduced rainfall and overuse of underground water, checking the water quality is one of the most important challenges discussed in various areas, such as Iran. Estimation of water quality using models such as neural network results in costs reduction and better management. The current study aims is to assess ground water quality using adaptive fuzzy neural network (ANFIS) in the west of Fars province during 2003 to 2013 period. Three methods including grid partitioning, sub-clustering and FCM with two models of Hybrid and back propagation were used to predict the quality of ground water for the study area. In this study, electrical conductivity (EC) and sodium adsorption ratio (SAR) were used to train the neural network. In addition, water quality class diagram Wilcox was used to train the network. In chemical pollution, according to Wilcox diagram, EC and SAR are the most important factors based on which waters can be classified in different classes such as very appropriate, suitable and unsuitable for agriculture. Results show that among various models provided to predict groundwater quality, Hybrid models in FCM method have the greatest accuracy for the prediction of water quality in the study area with a maximum R (0.99) and minimum error.

کلیدواژه‌ها [English]

  • Water quality
  • adaptive Neuro -Fuzzy inference system (ANFIS)
  • EC
  • SAR
 
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