مدلسازی و ارزیابی پتانسیل چشمه‌های آب زیرزمینی مبتنی بر GIS در حوزه آبخیز عمرک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران

2 گروه مهندسی آب، واحد کرمانشاه، دانشگاه ازاد اسلامی، کرمانشاه

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

4 گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران

چکیده

امروزه تأمین آب به منظور تحقق اهداف توسعۀ پایدار، یکی از مهم ترین چالش ها در اکثر کشورهای جهان است. به همین دلیل، تعیین مناطق دارای پتانسیل آب زیرزمینی، از ابزارهای مهم در حفاظت، مدیریت و بهره برداری از منابع آب به شمار می رود. بنابراین هدف از این پژوهش تهیه نقشه پتانسیل چشمه­های آب زیرزمینی با استفاده از یک مدل مشهور یادگیری ماشین (جنگل تصادفی) و یک مدل آماری (مدل نسبت فراوانی) و مقایسه کارایی این روشها در حوزه آبخیز عمرک، استان تهران می­باشد. ابتدا 18 عامل مؤثر در ظهور چشمه­ها شامل: سازندهای سنگ­شناسی، فاصله از گسل، تراکم گسل، طبقات ارتفاعی، درصد شیب، جهت شیب، فاکتور طول شیب، نقشه­های انحنا، فاصله از آبراهه، تراکم آبراهه، حداکثر ارتفاع، شاخص نمناکی، موقعیت شیب نسبی، بافت خاک، شاخص زبری سطح، شاخص همگرایی جریان و پوشش کاربری اراضی انتخاب شدند و نقشه آن‌ها در نرم­افزار ArcGIS10.5 و [1]SAGA تهیه گردید. پس از بررسی هم‌خطی و طبقه‌بندی لایه­های مؤثر با استفاده از روش شکستن طبیعی[2]، درصد فراوانی پتانسیل آب زیرزمینی در هر طبقه با استفاده از هم‌پوشانی نقشه پراکنش چشمه­ها با هرکدام از لایه‌ها به دست آمد. برای ارزیابی عملکرد مدل­های مذکور از منحنی مشخصه عملکرد نسبی(ROC) استفاده شد و میزان مساحت زیر منحنی (AUC) مدلهای جنگل تصادفی و نسبت فراوانی به ترتیب 88 و 72درصد به­دست آمد. نتایج نشان داد که هر دو روش تخمین­گرهای مناسبی برای تهیه نقشه پتانسیل چشمه آب زیرزمینی در منطقه مورد مطالعه هستند. اما مدل جنگل تصادفی با مقدار مساحت زیر منحنی بالاتر روش بهتری برای شناسایی و پهنه­بندی پتانسیل چشمه­های آب زیرزمینی معرفی گردید.
 
[1] System for automated geoscientific analyses
[2] Natural Break

کلیدواژه‌ها


عنوان مقاله [English]

GIS-based Groundwater Spring Potential Modelling and Assessment Mapping in the the Omarak Watershed

نویسندگان [English]

  • hamed karimi 1
  • fariborz yosefvand 2
  • saeid shabanlou 3
  • ahmad rajabi 4
1 Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
2 water Dept., kermanshah branch, islamic azad university, kermanshah, iran
3 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
4 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
چکیده [English]

Nowadays, in most countires, water supply in order to achieve the objectives of sustainable development is one of the most important challenges. Because of this, one of the important tools in the protection, management and exploitation of water resources, is to determin groundwater areas. Therefore, the purpose of this study, is to prepare the potential map of groundwater springs, using a well-known machine-learning model (i.e. random forest) and a statistical model (i.e. frequency ratio model) and comparing the efficiency of these methods in the Omarak watershed, Tehran Province. First, 18 factors influencing the emergence of springs including: lithological formations, the distance from the fault, fault density, elevation classes, slope percentage, slope direction, slope length factor, curvature maps, distance from the stream, stream density, maximum height, wetness index, relative slope position, soil texture, terrain roughness index, flow convergence index and land use cover were selected and their maps were prepared in the ArcGIS10.5 and SAGA systems. After the Multicollinearity test and classification of the effective layers, using the natural fracture method, then, the percentage of groundwater potential frequency in each layer obtained using the overlap of the distribution map of the springs with each of the layers. The relative operating characteristic (ROC) curve was used to evaluate the performance of the mentioned models and the area under the curve (AUC) of the random forest models and the frequency ratio were 88 and 72%, respectively. The results indicated that both methods are suitable to estimators to prepare the groundwater source potential map in the studied area. However, the random forest model with a higher area under the curve was introduced as a better method to identify and zoning the potential of groundwater springs.

کلیدواژه‌ها [English]

  • Groundwater Potential
  • Spring
  • Frequency Ratio model
  • stochastic forest model
  • relative operating characteristic curve
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