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تخمین هدایت هیدرولیکی و ارزیابی عدم قطعیت بین مدل‌ها و داده‌های ورودی توسط متوسط‌گیری بیزین از مدل‌های هوش مصنوعی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 3، دوره 52، شماره 9، آذر 1399، صفحه 2171-2190 اصل مقاله (2.02 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2019.15955.6087 | ||
| نویسندگان | ||
| یوسف حسن زاده* 1؛ مرجان معظم نیا2؛ سینا صادق فام3؛ عطاالله ندیری4 | ||
| 1دانشکده عمران-دانشگاه تبریز | ||
| 2گروه آب دانشکده عمران، دانشگاه تبریز | ||
| 3گروه عمران دانشگاه سراسری مراغه | ||
| 4دانشیار، دانشکده علوم زمین، دانشگاه تبریز، تبریز، ایران | ||
| چکیده | ||
| تخمین هدایت هیدرولیکی از مهمترین بخش های مطالعات هیدروژئولوژی بوده که در مدیریت آب های زیرزمینی حائز اهمیت است. اما به علت محدودیت‌های عملی، زمانی و یا هزینه ای، اندازه گیری مستقیم آن با دشواری همراه است. لذا استفاده از مدل‌های هوش مصنوعی با صرف هزینه کم و کارایی بالا می‌توانند جایگزین مناسبی برای این منظور باشند. از آنجا که داده های ورودی )شامل مقاومت عرضی، ضخامت آبخوان، هدایت الکتریکی و فاصله اقلیدسی( و تکنیک های آموزشی متفاوت در این نوع مدل‌ها به عنوان مهمترین عوامل ایجادعدم قطعیت هستند، لذا تاثیر منابع مختلف عدم قطعیت در خروجی باید درنظرگرفته شود. در این تحقیق روش میانگین‌گیری مدل بیزین (BMA )توسعه داده شده که شامل ترکیب مدل های شبکه عصبی مصنوعی، منطق فازی و نروفازی در تخمین هدایت هیدرولیکی و ارزیابی عدم قطعیت است. در مدل BMA ،وزن مدل ها توسط معیار اطلاعات بیزین (BIC ) تعیین شده و واریانس درون مدل ناشی از عدم قطعیت داده ورودی و واریانس بین مدل ها ناشی از عدم قطعیت مربوط به ذات مدل های هوش مصنوعی محاسبه می شود. در این مطالعه روش توسعه داده شده برای تخمین هدایت هیدرولیکی در آبخوان دشت ارومیه اعمال شده است. نتایج نشان می دهد اگرچه مقدار ضریب تعیین BMA نسبت به ضریب تعیین بهترین مدل، بالاتر نبوده ولی خروجی BMA حاصل اختصاص وزنهایی است که عدم قطعیت بین مدل ها و داده های ورودی را در نظر می گیرد. همچنین تاثیر تغییرات سطح آب زیرزمینی از زمان آزمون پمپاژ تا سال 1394بر مقادیر هدایت هیدرولیکی بررسی شده و نتایج تفاوت بسیار کمی در تغییرات هدایت هیدرولیکی نشان می دهد. | ||
| کلیدواژه‌ها | ||
| متوسط‌گیری مدل بیزین؛ هدایت هیدرولیکی؛ شبکه عصبی مصنوعی؛ منطق فازی؛ نروفازی | ||
| موضوعات | ||
| آب های زیر زمینی؛ مدیریت منابع آب | ||
| عنوان مقاله [English] | ||
| Hydraulic conductivity and uncertainty analysis of between-models and input data by using Bayesian model averaging of artificial intelligence model | ||
| نویسندگان [English] | ||
| yousef hassanzadeh1؛ Marjan Moazamnia2؛ Sina Sadeghfam3؛ Ata Allah Nadiri4 | ||
| 2Faculty of Civil Engineering/ University of Tabriz | ||
| 3Assistant Professor, Faculty of Engineering/ University of Maragheh | ||
| 4Associate Professor, Faculty of Earth Sciences/ University of Tabriz | ||
| چکیده [English] | ||
| The estimation of hydraulic conductivity is one of the most important part of hydrogeological studies which is important in groundwater management. But due to practical, time or cost constraints, direct measurement is difficult. Hence, the using artificial intelligence models with low cost and high performance can be an appropriate alternative for this purpose. Since input data and different training techniques in these models are the most important source of uncertainty, the effect of various sources of uncertainty in output should be considered. In this research a Bayesian Model Averaging (BMA) are developed which includes the model combination of artificial neural network, fuzzy logic and neuro-fuzzy to estimate hydraulic conductivity and uncertainty analysis. In the BMA model, the weight of the models is determined by the Bayesian information criterion (BIC), and the within-model variance, steam from the uncertainty of input data and the between-model variance steam from uncertainty associated with the nature of the artificial intelligence model are calculated. In this study, the developed method has been applied to estimate the hydraulic conductivity in the Urmia aquifer. The results show that although the determination coefficient of BMA is not higher than the determination coefficient of the best model, the output of the BMA is the result of assigning weights that take into account the uncertainty between the models and the input data. Also, the effect of groundwater level variation on estimated hydraulic conductivity from pumpage test up to 2015 was evaluated and the result indicated an insignificant changes in hydraulic conductivity. | ||
| کلیدواژه‌ها [English] | ||
| Bayesian Model Averaging, Hydraulic Conductivity, Artificial Neural Network, Fuzzy Logic, neuro-fuzzy | ||
| مراجع | ||
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