Comparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case study in Birjand plainComparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case

Malekinia, Mehran; Javid, Amir Hossein; Rahmati, Seyedeh Hoda; Jahangir, Alireza

doi:10.22077/jaaq.2026.10568.1133

Document Type : Original Article

Authors

¹ Ph.D. Student, Department of Environmental Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² professor. Department of Natural Resources and Environment, SR.C., Islamic Azad University, Tehran, Iran

³ Assistant professor. Department of Natural Resources and Environment, SR.C., Islamic Azad University, Tehran, Iran

⁴ Assistant professor, Department of Water Science and Engineering, Payame Noor University, Tehran, Iran.

10.22077/jaaq.2026.10568.1133

Abstract

با توجه به اهمیت تعیین حجم ذخایر منابع آب زیرزمینی برای مدیریت پایدار آب در مناطق خشک و نیمه‌خشک، این مطالعه به بررسی رویکردهای پیش‌بینی و شبیه‌سازی تراز آب زیرزمینی با مدل‌های یادگیری ماشین (LSTM، Random Forest و مدل ترکیبی Stacking Ensemble) در مقایسه با مدل عددی MODFLOW در 17 چاه مشاهده‌ای دشت بیرجند پرداخت. داده‌های سری زمانی از مارس 2008 تا جولای 2025 (204 نمونه) با معیارهای آماری تحلیل شدند. پیش‌پردازش داده‌ها شامل نرمال‌سازی و فیلتر صاف‌سازی برای حذف نویز بود. تحلیل حساسیت با روش‌های SHAP و مونت‌کارلو چاه‌های کلیدی را شناسایی کرد. مدل Stacking Ensemble، با تلفیق خروجی‌های کالیبره‌شده MODFLOW و پیش‌بینی‌های مدل‌های یادگیری ماشین، در چاه 1 بالاترین دقت را با R²=0.9783، RMSE=0.0322 متر و MAPE=0.0017% نشان داد، در حالی که MODFLOW با R²=0.968 و RMSE=3.17 متر در چاه‌های 5، 10 و 15 خطای بیشتری داشت. نمودارهای تیلور برتری مدل استکینگ را با ضریب همبستگی 0.9783 و انحراف معیار نرمال‌شده 0.812 نسبت به LSTM (ضریب همبستگی 0.9765، انحراف معیار 0.845) و Random Forest (ضریب همبستگی 0.9398، انحراف معیار 1.344) تأیید کردند. این رویکرد ترکیبی نیاز به داده‌های ورودی گسترده را کاهش می‌دهد، دقت پیش‌بینی را در آبخوان‌های ناهمگن با افت سالانه 40-60 سانتی‌متر بهبود می‌بخشد، و ابزاری کارآمد برای مدیریت پایدار منابع آب در دشت بیرجند و مناطق مشابه با شرایط هیدرولوژیکی پیچیده فراهم می‌کند.

Keywords

Main Subjects

ًQanat and aquifer

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Journal of Aquifer and Qanat

Comparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case study in Birjand plainComparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case

References

References

Volume 6, Issue 2
January 2025
Pages 257-281

Comparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case study in Birjand plainComparison of numerical models and machine learning algorithms with groundwater level prediction approach: A case

References

References

Volume 6, Issue 2January 2025Pages 257-281

Volume 6, Issue 2
January 2025
Pages 257-281