بهینه‌یابی شبکه پایش سطح آب زیرزمینی در آبخوان ارومیه با استفاده از نقشه خود سازمانده

نویسندگان

1 دانشگاه خوارزمی

2 گروه زمین‌شناسی، پردیس علوم، دانشگاه یزد

3 گروخ زمین شناسی کاربردی، دانشکده علوم زمین، دانشگاه خوارزمی

چکیده

در این مطالعه از روش نوین نقشه‌های خودسازمانده (SOM) برای خوشه‌بندی پیزومترهای موجود در آبخوان ارومیه و شناسایی پیزومترهایی با رفتار مشابه و به عبارتی دیگر، تعیین شبکه پایش بهینه در این آبخوان استفاده شد. به این منظور، ابتدا به نرمال‌سازی داده‌های ماهیانه تراز آب زیرزمینی در یک بازه 13 ساله (1381-1394) پرداخته شد و سپس الگوریتم خوشه‌بندی غیرسلسله مراتبی K-means برای تعیین تعداد خوشه‌های بهینه مورد استفاده قرار گرفت. در ادامه به کمک مدل SOM که از پرکاربردترین مدل‌های شبکه عصبی در خوشه‌بندی است؛ عملیات پیش‌پردازش مکانی جهت خوشه‌بندی مکانی پیزومترها انجام شد و نتایج خوشه‌بندی با استفاده از نقشه هم عمق سطح آب زیرزمینی مورد بررسی قرار گرفت. پیزومترهای نماینده هر خوشه از طریق به کارگیری فاصله اقلیدسی پیزومترها از یکدیگر تعیین شد. با توجه به نتایج خوشه‌بندی و سازگاری بسیار مناسب آن با تغییرات سطح آب زیرزمینی در محل هر پیزومتر، باید عنوان نمود که الگوریتم خوشه‌بندی SOM از قابلیت بالایی برای خوشه‌بندی برخوردار است. این نوع شبکه عصبی قادر به کشف الگوهای مناسبی از داده‌ها است که می‌تواند به شناسایی ویژگی‌های اعضاء هر خوشه کمک کند. بنابراین، به نظر می‌رسد از الگوریتم خوشه بندی SOM می‌توان در تحلیل رفتار کمی آبخوان (تغییرات تراز آب زیرزمینی) و یافتن پیزومترهای نماینده معدود استفاده کرده و به این ترتیب امکان ارزیابی قابل قبول رفتار آبخوان در زمان کمی و با صرف کمترین هزینه فراهم آورد.

کلیدواژه‌ها


عنوان مقاله [English]

Optimization of Groundwater Monitoring Network in Urmia Aquifer Using Self-Organizing Map

نویسندگان [English]

  • Mohammad Nakhaei 1
  • Vahab Amiri 2
  • Marzieh Amiri 3
1 Kharazmi University
2 Dep. of Geology, Faculty of Sciences, Yazd University
3 Dep. of Applied Geology, Faculty of earth sciences, Kharazmi university
چکیده [English]

In this study, the new method of self-organized maps (SOM) was used to cluster the piezometers in the Urmia aquifer and identify piezometers with similar behavior, in consequence the optimal groundwater monitoring network was determined. To achieve this goal, at first the normalization of monthly groundwater level data over a period of 13 years (2002-2015) was done and then the K-means non-hierarchical clustering algorithm was used to determine the number of optimal clusters. Then, using the SOM model which is one of the most widely used neural network models in the clustering techniques, spatial preprocessing operations were performed for all piezometers and the clusters were detected using a groundwater depth map. Also, the representative piezometers of each cluster were determined using the piezometers Euclidean distance. According to the results of clustering and its good compatibility with changes in groundwater level at each piezometer, it should be noted that the SOM clustering algorithm has a high capability for clustering. Results show that this type of network is able to detect the appropriate data patterns that can help identify the characteristics of members of each cluster. Therefore, it seems that the SOM clustering algorithm can be used to analyze the quantitative behavior of the aquifer (groundwater level changes) and find a few representative piezometers, thus providing an acceptable assessment of aquifer behavior in a short time and at the lowest cost.

کلیدواژه‌ها [English]

  • Groundwater
  • Clustering
  • Neural network
  • K-means
  • SOM
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