نشریه علوم زمین خوارزمی

نشریه علوم زمین خوارزمی

طراحی ماشین مرکب هوش مصنوعی نظارت شده جهت تخمین پارامترهای هیدرودینامیکی آبخوان‌های محبوس

نویسنده
طاهره آذری
دانشگاه خوارزمی
10.22034/KJES.2025.11.1.106211
چکیده
تخمین دقیق پارامترهای هیدرودینامیکی، اولین گام جهت توسعه پایدار آبخوان است. از زمان Theis (1935) ، جهت تخمین پارامترهای آبخوان از روش انطباق منحنی تیپ (Type Curve Matching Technique _ TCMT) استفاده می‌شد. این روش همراه با خطاهای گرافیکی است. در این تحقیق یک ماشین مرکب هوش مصنوعی نظارت شده جهت از بین بردن خطا و تخمین دقیق پارامترهای هیدرودینامیکی آبخوان های محبوس با توانایی بالا در تقریب توابع به عنوان جایگزینی برای روش مرسومTCMT و سایر روش های هوش مصنوعی استفاده گردید. در این تحقیق داده‌های آزمون پمپاژ به عنوان مولفه های ورودی و مختصات نقطه انطباق بهینه به عنوان مولفه خروجی در نظر گرفته شد. همچنین جهت کاهش ابعاد مولفه های ورودی، از تکنیک آنالیز مولفه های اصلی (PCA) استفاده گردید. سپس مختصات نقطه انطباق با حل تحلیلی تایس (1935) ترکیب شده و پارامترهای آبخوان محاسبه گشت. جهت توسعه ماشین مرکب در مرحله اول سه مدل شبکه عصبی مصنوعی با الگوریتم های آموزش مختلفLevenberg–Marquardt (LM), gradient descent (GD), resilient back-propagation (RP) جهت تعیین نقطه انطباق و تخمین پارامترهای هیدرودینامیکی آبخوان‌ محبوس تدوین شد که بر اساس نتایج حاصل از مدلسازی، تمامی مدل‌ها تقریب مناسبی از پارامترهای هیدرودینامیکی آبخوان محبوس نشان داده‌اند. سپس در مرحله دوم با توجه به پیچیدگی سیستم‌های هیدروژئولوژیکی، ماشین مرکبی متشکل از سه مدل هوش مصنوعی طراحی شده ساخته شد که از توانایی های هر سه مدل جهت تعیین پارامترهای هیدرودینامیکی آبخوان های محبوس استفاده نموده است. خروجی مدل‌های مورد استفاده با ترکیب کننده غیرخطی نظارت شده با هم ترکیب شده و خروجی نهایی ماشین مرکب (نقطه انطباق بهینه) با دقت بسیار بالایی تعیین گردید. نتایج نشان داد مدل ماشین مرکب پیشنهاد شده روشی دقیق تر و جایگزین بهتری نسبت به روش های TCMT و روش های هوش مصنوعی در تعیین نقطه انطباق بهینه و تخمین پارامترهای هیدرودینامیکی آبخوان محبوس می باشد.



کلیدواژه‌ها
شبکه عصبی مصنوعی
ماشین مرکب هوش مصنوعی نظارت شده
آزمون پمپاژ
روش انطباق منحنی تیپ
پارامترهای آبخوان محبوس

عنوان مقاله English

Design of a supervised artificial intelligence committee machine to estimate hydrodynamic parameters of confined aquifers

نویسنده English

Tahereh Azari
Kharazmi university
چکیده English

Accurate estimation of hydrodynamic parameters is essential for sustainable aquifer development. Since Theis (1935), the Type Curve Matching Technique (TCMT) has been used to estimate aquifer parameters. This method is associated with graphical errors. In this study, a supervised AI committee machine was used to eliminate errors and accurately estimate the hydrodynamic parameters of confined aquifers with high ability to approximate functions as an alternative to the conventional TCMT method and existing AI methods. In this study, pumping test data were considered as input components and the coordinates of the optimal point were considered as the output. To reduce the dimensions of the input components, the principal component analysis (PCA) technique was used. Then, the matching point coordinates were combined with the analytical solution of Theis (1935) and the values of the aquifer parameters were calculated. To develop this machine, in the first step, three ANNs with different training algorithms, Levenberg–Marquardt (LM), gradient descent (GD), resilient back-propagation (RP), were developed to determine the match point and estimate the hydrodynamic parameters of the confined aquifer. Based on the modeling results, all models showed a good approximation of the hydrodynamic parameters of the confined aquifer. Then, in the second step, considering the complexity of hydrogeological systems, a committee machine consisting of three artificial intelligence models was designed and built, which used the capabilities of all three models to determine the hydrodynamic parameters of the confined aquifers. The models' outputs were combined using a supervised nonlinear combiner, yielding highly accurate final results. The results showed that the proposed committee machine model is more accurate, and better alternative to TCMT methods and artificial intelligence methods in determining the optimal match point and estimating the hydrodynamic parameters of the confined aquifer.

کلیدواژه‌ها English

Artificial neural network
supervised artificial intelligence committee machine
pumping test
type curve matching method
confined aquifer parameters
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