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آمار
| تعداد نشریات | 283 |
| تعداد نشریات سازمان | 81 |
| تعداد شماره‌ها | 2,726 |
| تعداد مقالات | 31,041 |
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کاربرد الگوریتم‌های درخت وایازی تقویت‌شده، درخت مدل پشتیبان، و جنگل تصادفی برای ارزیابی احتمال آب‌های زیرزمینی | ||
| پژوهش های آبخیزداری | ||
| مقاله 5، دوره 35، شماره 3 - شماره پیاپی 136، مهر 1401، صفحه 44-59 اصل مقاله (2.53 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.356517.1440 | ||
| نویسندگان | ||
| سید احمد اسلامی نژاد1؛ مبین افتخاری* 2؛ محمد اکبری3؛ هادی بیات1؛ وریا برقی1 | ||
| 1دانش‌آموخته ی کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکده ی مهندسی نقشه برداری و اطلاعات مکانی، دانشگاه تهران، تهران | ||
| 2دانش آموخته ی کارشناسی ارشد مهندسی عمران آب و سازه های هیدرولیکی و عضو باشگاه پژوهشگران جوان و نخبگان، واحد مشهد، دانشگاه آزاد اسلامی، مشهد | ||
| 3دانشیار گروه مهندسی عمران، دانشگاه بیرجند | ||
| چکیده | ||
| به‌دلیل رشد جمعیت و صنعتی‌شدن در منطقه‌های گوناگون جهان، از آب‌های زیرزمینی به‌طور مهارناپذیری بهره‌برداری می‌شود. هدف این پژوهش، ارزیابی احتمال آب‌های زیرزمینی با الگوریتم‌های پیشرفتهی یادگیری ماشین و با معیارهای پستی‌بلندی، آب‌شناسی، محیطی و زمین‌شناسی است. برای انجام این کار سه الگوریتم پیشرفتهی یادگیری ماشین شامل درخت وایازی تقویت‌شده، درخت مدل پشتیبان، و جنگل تصادفی به‌کار برده‌شد. داده‌های آب‌شناسی 37 چاه آب زیرزمینی در دشت بیرجند، استان خراسان جنوبی، جمع‌آوری، و با انتخاب تصادفی به نسبت 70 به 30 به مجموعه‌ی داده‌های آموزشی و اعتبارسنجی تقسیم کرده‌شد. نقشه‌های احتمال آب زیرزمینی با سه الگوریتم تهیه شد. برای اعتبارسنجی الگوریتم‌های پیش‌بینی احتمال آب زیرزمینی، سطح زیر منحنی و معیارهای آماری نرخ پیش‌بینی‌شده‌ی مثبت، نرخ پیش‌بینی‌شده‌ی منفی، حساسیت، ویژگی، و دقت به‌کار برده‌شد. نتیجه نشان داد که درخت مدل پشتیبان (0/865 =AUC) کارکرد بهتری در پیش‌بینی احتمال آب زیرزمینی منطقه دارد. | ||
| کلیدواژه‌ها | ||
| سامانه‌ی اطلاعات جغرافیایی (جی‌آی‌اس)؛ احتمال آب زیرزمینی؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Using Boosted Regression Tree, Logistic Model Tree, and Random Forest Algorithms to Evaluate the Groundwater Potential | ||
| نویسندگان [English] | ||
| Seyed Ahmad Eslaminezhad1؛ Mobin Eftekhari2؛ Mohammad Akbari3؛ Hadi Bayat1؛ Wrya Barghi1 | ||
| 1Master of Science (M.Sc.), Department of Surveying Engineering, Faculty of Surveying Engineering and Geospatial Information, University of Tehran, Tehran, Iran | ||
| 2Master of Science (M.Sc.), Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran | ||
| 3Associate Professor of Civil Engineering Department, University of Birjand | ||
| چکیده [English] | ||
| Groundwater is exploited uncontrollably due to population growth and industrialization in different parts of the world. The purpose of this study is to evaluate the groundwater potential by advanced machine learning algorithms using topographical, hydrological, environmental, and geological criteria. To do this, three advanced machine learning algorithms were used, including Boosted Regression Tree (BRT), Logistic Model Tree (LMT), and Random Forest (RF). Therefore, for implementation, geo-hydrological data of 37 groundwater wells in Birjand plain of South Khorasan province were collected and randomly selected in a ratio of 70 to 30 were divided into training and validation data sets. Finally, groundwater potential maps were prepared using BRT, LMT, and RF algorithms. In order to validate the groundwater potential prediction algorithms, the area under the curve (AUC) and the statistical criteria of positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the LMT model (AUC = 0.865) has a better performance than the BRT and RF models in predicting the groundwater potential of the study area. | ||
| کلیدواژه‌ها [English] | ||
| Geographical information system (GIS), groundwater potential, machine learning | ||
| مراجع | ||
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سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب