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توسعۀ مدل DRASTIC با استفاده از هوش مصنوعی در پتانسیل آلودگی آبخوان مناطق نیمه خشک | ||
اکوهیدرولوژی | ||
مقاله 4، دوره 8، شماره 3، مهر 1400، صفحه 651-665 اصل مقاله (1.48 M) | ||
نوع مقاله: پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ije.2021.323188.1501 | ||
نویسندگان | ||
مبین افتخاری1؛ سید احمد اسلامی نژاد2؛ علی حاجی الیاسی3؛ محمد اکبری* 4 | ||
1دانشآموختۀ کارشناسی ارشد مهندسی عمران آب و سازههای هیدرولیکی و عضو باشگاه پژوهشگران جوان و نخبگان، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران | ||
2دانش آموختۀ کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکدۀ مهندسی نقشه برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران | ||
3دانش آموختۀ کارشناسی ارشد، گروه مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران | ||
4دانشیار 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 | ||
آسیب پذیری؛ آلودگی نیترات؛ ماشین بردار پشتیبان؛ مدل DRASTIC | ||
مراجع | ||
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