توسعۀ مدل DRASTIC با استفاده از هوش مصنوعی در پتانسیل آلودگی آبخوان مناطق نیمه ‏خشک

نوع مقاله : پژوهشی

نویسندگان

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

2 دانش ‏آموختۀ کارشناسی ارشد، گروه مهندسی نقشه‏ برداری، دانشکدۀ مهندسی نقشه‏ برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران

3 دانش ‏آموختۀ کارشناسی ارشد، گروه مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

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

چکیده

به دلیل رشد اقتصادی سریع و بهره‏ برداری بیش از حد از آب ‏های زیرزمینی، مسئلۀ آلودگی نیترات در آب‏ های زیرزمینی بسیار جدی شده ‏است. هدف اصلی این مطالعه، توسعۀ مدل DRASTIC برای شناسایی آسیب ‏پذیری آب‏های زیرزمینی در برابر آلودگی نیترات است. بنابراین، مدل استاندارد DRASTIC با در نظر گرفتن عامل کاربری اراضی (مدل DRASTIC-LU) ‏برای به نمایش گذاشتن آسیب ‏پذیری آب‏های زیرزمینی ارائه شد. نوآوری تحقیق حاضر، توسعۀ مدل‏ های DRASTIC و DRASTIC-LU توسط ماشین بردار پشتیبان (SVM) ‏به منظور جلوگیری از خطای روش‏های همپوشانی و شاخص است. برای پیاده‏ سازی و اعتبارسنجی مدل‏ها، 21 نمونه چاه مشاهداتی در آبخوان دشت بیرجند جمع‏ آوری شدند. مقادیر RMSE مربوط به مدل‏های DRASTIC، DRASTIC-LU، DRASTIC+SVM و DRASTIC-LU+SVM به‌ترتیب 821/0، 743/0، 612/0 و 490/0 ‌شد که نشان داد مدل‏های ترکیبی با استفاده از SVM همبستگی بهتری را بین مقدار آسیب ‏پذیری و آلودگی نیترات نشان می‏ دهد. همچنین، مشخص شد که مدل DRASTIC-LU+SVM برای ارزیابی آسیب ‏پذیری آب‏ های زیرزمینی در برابر نیترات دقت بیشتری دارد.

کلیدواژه‌ها

موضوعات


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دوره 8، شماره 3
مهر 1400
صفحه 651-665
  • تاریخ دریافت: 29 بهمن 1399
  • تاریخ بازنگری: 26 خرداد 1400
  • تاریخ پذیرش: 28 خرداد 1400
  • تاریخ اولین انتشار: 01 تیر 1400
  • تاریخ انتشار: 01 مهر 1400