شبیه‌سازی تراز آب زیرزمینی با استفاده از مدل ترکیبی موجک-ماشین آموزش نیرومند خودتطبیقی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه منابع طبیعی، دانشکده منابع طبیعی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران

2 استادیار، گروه معماری، دانشکده هنر و معماری، واحد علوم و تحقیقات ، دانشگاه آزاد اسلامی، تهران، ایران

3 دانشیار، گروه مهندسی آب، دانشکده کشاورزی، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران

چکیده

در مطالعه حاضر، با استفاده از روش­های نوین ماشین آموزش نیرومند خود تطبیقی  (SAELM)و موجک-ماشین آموزش نیرومند خود تطبیقی  (WA-SAELM) تراز آب زیرزمینی در منطقه کبودر آهنگ واقع در استان همدان مورد بررسی قرار گرفت. در ابتدا با استفاده از تابع خود همبستگی، تاخیرهای موثر شناسایی شده و سپس با استفاده از این تاخیرها برای هر یک از روش­های SAELM و WA-SAELM، 10 الگوی متمایز ورودی توسعه داده شد. با ارزیابی نتایج مدل­های مذکور، WA-SAELM به­عنوان مدل برتر معرفی شد که تجزیه و تحلیل نتایج شبیه­سازی نشان دهنده دقت بالای مدل برتر در تخمین تراز آب زیرزمینی بود. مقادیر ضریب همبستگی (R)، ریشه میانگین مربعات خطا (RMSE) و ضریب بهره­وری نش-ساتکلیف (NSC) برای مدل برتر به­ترتیب برابر با 969/0، 358/0 و 939/0 محاسبه گردید.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Simulation of Groundwater Level Using the Hybrid Model Wavelet-Self Adaptive Extreme Learning Machine

نویسندگان [English]

  • maryam malekzadeh 1
  • saeid kardar 2
  • saeid shabanlou 3
1 Assistance Prof., Department of Environment, Faculty of enviroment, Tehran North Branch, Islamic Azad University, Tehran, Iran
2 Assistance Prof., Department of Architecture, Faculty of Art and Architecture, Science and Research Branch, Islamic Azad Univ., Tehran, Iran.
3 Associ. Prof., Dept. of Water Eng., Faculty of Agriculture, Kermanshah Branch, Islamic Azad Univ., Kermanshah, Iran.
چکیده [English]

In present study, the groundwater level of the Kabodarahang region located in Hamadan Province was simulated using novel techniques such as Self-Adaptive Extreme Learning Machine (SAELM) and Wavelet-Self-Adaptive Extreme Learning Machine (WA-SAELM). Firstly, the effective lags were detected using the autocorrelation function and then ten models were developed for each SAELM and WA-SAELM methods. By evaluating the results of the models, WA-SAELM was introduced as the superior method. The analysis of the simulation results showed that the superior model had a high accuracy in estimating the groundwater level. For the superior model, the correlation coefficient (R), Root Mean Squared Error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were calculated to be 0.969, 0.358 and 0.939, respectively.

کلیدواژه‌ها [English]

  • Ground water
  • Self-adaptive extreme learning machine
  • Wavelet transform
  • Wavelet-Self-Adaptive Extreme Learning Machine
  • Kabodarahang
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