پیش‌بینی مناطق بالقوه آب‌ زیرزمینی با استفاده از روش‌های هوش مصنوعی ترکیبی (مطالعه موردی: دشت بیرجند)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد مهندسی عمران آب و سازه‌های هیدرولیکی و عضو باشگاه پژوهشگران جوان و نخبگان، واحد مشهد، دانشگاه آزاد

2 دانش‌آموخته کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران

3 گروه مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی ، تهران ، ایران

4 دانشیار گروه مهندسی عمران، دانشگاه بیرجند، بیرجند، ایران

چکیده

آب‌های زیرزمینی یکی از مهم‌ترین منابع با ارزش برای استفاده جوامع، کشاورزی و صنایع هستند. در مطالعه حاضر، سه مدل هوش مصنوعی جدید شامل مدل آدابوست واقعی بهبود یافته (MRAB)، مدل بگینگ (BA) و مدل جنگل چرخشی (RF) توسط مدل طبقه‌بندی‌کننده پایه درخت عملکردی (‏FT) ‏برای پیش‌بینی مناطق بالقوه آب‌های زیرزمینی در منطقه دشت بیرجند توسعه داده شده‌اند. لذا جهت پیاده‌سازی، داده‌های ژئوهیدرولوژیکی 37 حلقه چاه آب زیرزمینی و 10 عامل توپوگرافی، هیدرولوژی و زمین‌شناسی مورد استفاده قرار گرفت. عملکرد این مدل‌ها با استفاده از سطح زیر منحنی (AUC) و سایر شاخص‌های آماری مورد ارزیابی قرار گرفت. نتایج نشان داد که هر چند تمامی مدل‌های ترکیبی توسعه داده شده در این تحقیق دقت پیش‌بینی را افزایش دادند، اما مدل MRAB-FT (742/0‏AUC=)‏ دقت بالاتری را در پیش‌بینی مناطق بالقوه آب‌های زیرزمینی در منطقه دشت بیرجند دارد. تهیه نقشه دقیق از مناطق بالقوه آب زیرزمینی، با حفظ تعادل بین مصرف و بهره‌برداری، به تغذیه مناسب آبخوان برای استفاده بهینه از منابع آب زیرزمینی کمک خواهد کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting Groundwater Potential Areas Using Hybrid Artificial Intelligence Methods (Case Study: Birjand Plain)

نویسندگان [English]

  • Mobin Eftekhari 1
  • Seyed Ahmad Eslaminezhad 2
  • Ali Haji Elyasi 3
  • Mohammad Akbari 4
1 MSc. graduate, Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2 MSc. graduate, Department of Surveying Engineering, Faculty of Surveying Engineering and Spatial Information, University of Tehran, Tehran, Iran
3 Department of Civil Engineering, K. N. Toosi University of Technology, Tehran,Iran
4 Dept. of Civil Engineering, University of Birjand, Birjand, Iran
چکیده [English]

Groundwater is one of the most valuable resources for communities, agriculture, and industry. In the present study, three new artificial intelligence models, including Modified Real AdaBoost (MRAB), Bagging model (BA), and Rotation Forest model (RF), have been developed by the Functional Tree Base Classifier (FT) model to predict groundwater potential in Birjand plain area. Therefore, for implementation, geo-hydrological data of 37 groundwater wells and ten factors of topography, hydrology, and geology were used. The performance of these models was evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that although all the hybrid models developed in this study increased the prediction accuracy, MRAB-FT model (AUC = 0.742) has higher accuracy in predicting potential groundwater areas in Birjand plain. Accurate mapping of groundwater potential areas while maintaining a balance between consumption and operation will help feed the aquifer for optimal use of groundwater resources.

کلیدواژه‌ها [English]

  • Groundwater Potential
  • Artificial Intelligence
  • Semi-arid areas
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