مدل‌سازی توان منابع آب زیرزمینی با استفاده از مدل های یادگیری ماشینی

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی محیط‌زیست گرایش منابع آب، پردیس بین المللی کیش، دانشگاه تهران

2 استاد، دانشکده ی محیط‌زیست، دانشگاه تهران

3 دانشیار، دانشکده ی محیط‌زیست، دانشگاه تهران

10.22092/wmrj.2023.360675.1506

چکیده

مقدمه و هدف
توان­یابی منابع آب زیرزمینی یکی از اصل‌های پایه در مدیریت منابع آب است. هدف این پژوهش توان­ یابی آب زیرزمینی با استفاده از مدل ­های یادگیری ماشین بردار پشتیبان (SVM) و همچنین دستورالعمل­ های فراکاوشی (مدل ترکیبی (هیبریدی) ماشین بردار پشتیبان و دستورالعمل بهینه­ سازی فراکاوشی زنبورعسل (SVM-BA) و مدل ترکیبی ماشین بردار پشتیبان و دستورالعمل بهینه ­سازی فراکاوشی ازدحام ذرات (SVM-PSO) است.
مواد و روش­ ها
در این پژوهش در منطقه‌ی بجنورد عامل‌های بلندی، شیب، جهت، شاخص رطوبت پستی‌بلندی، فاصله از آبراهه، تراکم زهکشی، فاصله از گسل، سنگ­ شناسی، شاخص موقعیت پستی‌بلندی، شاخص ناهمواری زمین، موقعیت شیب نسبی و شاخص هم گرایی جریان انتخاب شدند. از شرکت آب منطقه ­ای اطلاعات موقعیت 359 چشمه دریافت شد. دستورالعمل تقسیم ­بندی تصادفی برای تقسیم نقطه‌های آموزشی (70%) و نقطه‌های اعتبارسنجی (30%) استفاده شد. براساس تحلیل حساسیت حذفی، اندازه‌ی اهمیت و مشارکت متغیرهای ورودی در توان ­یابی آب زیرزمینی مشخص شد. ارزیابی دقت مدل‌ها در دو مرحله‌ی آموزش و اعتبارسنجی براساس روش منحنی مشخصه عامل گیرنده (ROC) انجام شد.
نتایج
ارزیابی دقت مدل‌ها براساس معیار ارزیابی مساحت زیرمنحنی عامل گیرنده (AUC) نشان داد که دقت پیش­ بینی مدل ترکیبی ماشین بردار پشتیبان و دستورالعمل بهینه ­سازی فراکاوشی ازدحام ذرات (SVM-PSO) 945/0 بیشتر از دیگر مدل­ ها (SVM: 0.918 و SVM-BA: 0.932) بود. براساس نتایج مدل برتر در این پژوهش 7/75% از سطح منطقه طبقه‌ی توان زیاد و 38/66% از سطح منطقه طبقه‌ی توان خیلی‌زیاد را کسب کردند. از میان عامل‌های، موقعیت شیب نسبی (14/5%)، فاصله از گسل (13/4%) و سنگ ­شناسی (12/3%) در پیش ­بینی توان آب زیرزمینی بیشترین اهمیت را داشتند.
بحث و نتیجه گیری
براساس نتایج این پژوهش، عملکرد مدل ماشین بردار پشتیبان زیاد بود و دو دستورالعمل بهینه ­سازی فراکاوشی زنبورعسل و فراکاوشی ازدحام ذرات موجب تقویت قدرت پیش­بینی مدل شدند. همچنین مدل­ های یادگیری ماشینی می­ توانند ارتباط میان عامل‌های محیطی و آب‌دهی چشمه ­ها را شناسایی کنند و با به‌کارگیری داده های موجود، نقش آن­ ها را تعیین کنند. عامل موقعیت شیب نسبی به‌عنوان مهم‌ترین متغیر و عامل فاصله از گسل نیز به‌عنوان دومین متغیر مهم در این پژوهش مشخص شدند. نتایج پژوهش نشان داد که در تغذیه‌ی بخش­ های زیرسطحی، ذخیره و جریان آب زیرزمینی، گسل­ های منطقه نقش مهمی داشتند. با کاربرد مدل در شناسایی وضعیت توان آب زیرزمینی عامل سنگ­ شناسی نیز به‌عنوان سومین متغیر مهم معرفی شد. در این پژوهش، با پیشنهاد نقشه‌ی توان آب زیرزمینی، امکان برنامه ­ریزی و تدقیق آمایش سرزمین برای آبخیز بجنورد فراهم شد.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling Groundwater Potential Using Machine Learning Models

نویسندگان [English]

  • Ahmad Salamat 1
  • Mojtaba Ardestani 2
  • Bahram Malekmohammadi 3
1 Ph.D. Student in Environmental Engineering, Water resources, Kish International Campus, University of Tehran
2 Professor, Faculty of Environment, University of Tehran
3 Associate Professor, Faculty of Environment, University of Tehran
چکیده [English]

Introduction
Finding the potential of groundwater resources is one of the basic principles in water resources management. The aim of this research is to determine the potential of groundwater using support vector machine learning (SVM) models as well as metaheuristic algorithms (hybrid support vector machine model and the bee metaheuristic optimization algorithm (SVM-BA) and hybrid model of the support vector machine and particle swarm optimization algorithm (SVM-PSO).
Materials and methods
The factors of elevation, slope, aspect, topographic humidity index, distance from stream, drainage density, distance from fault, lithology, topographic position index, land roughness index, relative slope position and flow convergence index were selected in Bojnurd region. Information on the location of 359 springs was received from the regional water company. Random division algorithm was used to divide training points (70%) and validation points (30%). Based on the removal sensitivity analysis, the importance and contribution of the input variables in determining the groundwater potential were determined. The accuracy of the models was evaluated in two stages of training and validation based on the receiver operating characteristic (ROC) curve method.
Results
The evaluation of the accuracy of the models based on the evaluation criteria of the area under curve (AUC) showed that the prediction accuracy of the hybrid model of the support vector machine and the particle swarm optimization algorithm (SVM-PSO) is 0.945 more than other models (SVM: 0.918 and SVM-BA: 0.932). Based on the results of the superior model, the high potential class and the very high potential class accounted for 7.75% and 38.66% of the area respectively. Among the factors, relative slope position with 14.5%, distance from the fault with 13.4% and lithology with 12.3% were the most important in predicting groundwater potential.
Discussion and Conclusion
Based on the results of this research, the support vector machine model has a high performance, and two optimization algorithms, the bee metaheuristic and particle swarm optimization algorithm, strengthen the predictive power of the model. Also machine learning models can identify the relationship between the environmental factors and the water supply of the springs and determine their role by using the available data. The relative slope position factor was identified as the most important variable and the distance from the fault factor was considered as the second most important variable in the present study. The results of the research showed that the faults in the region play an important role in aquifer recharge, storage and flow of groundwater. The lithological factor was also introduced by the model as the third important variable in identifying the state of groundwater potential. In this research, by presenting the groundwater potential map, it is possible to plan and verify land use planning for the Bojnurd watershed.

کلیدواژه‌ها [English]

  • Bojnurd
  • groundwater
  • machine learning
  • support vector machine
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