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ارزیابی کارایی روشهای شتابدهنده یادگیری ماشین بهمنظور تخمین شاخص کیفی آب رودخانه زایندهرود | ||
تحقیقات آب و خاک ایران | ||
دوره 56، شماره 5، مرداد 1404، صفحه 1355-1378 اصل مقاله (2.58 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2025.392173.669906 | ||
نویسندگان | ||
الهام فاضل نجف آبادی* ؛ محمد شایان نژاد | ||
گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان، ایران | ||
چکیده | ||
باتوجهبه پدیده تغییر اقلیم، گرمایش کره زمین و کاهش منابع آب، کیفیت آبهای سطحی بهعنوان یکی از مهمترین منابع آبی در جهان مورد توجه مهندسین رودخانه قرار دارد. ازآنجاکه پرکاربردترین شاخص سنجش کیفیت آب شاخص WQI است؛ هدف و اهمیت این تحقیق مدلسازی شاخص کیفیت آب به کمک دو روش شتابدهنده یادگیری ماشین Gradient Boosting و XGBoost در رودخانه زایندهرود انجامگرفته است. در این تحقیق ابتدا بر اساس دادههای کیفیت آب، شاخص کیفیت آب (NSFWQI) محاسبه، و در ادامه بهمنظور مدلسازی، از دادههای ورودی شامل ویژگیهای کیفی آب ۸ ایستگاه در یک دوره ۳۱ساله و همچنین شاخص کیفیت آب محاسبه شده رودخانه استفاده شد. در این تحقیق برای مدلسازی در محیط برنامهنویسی پایتون کدنویسی شده، و در مرحله آموزش ۸۰ درصد دادهها و در مرحله ارزیابی ۲۰ درصد باقیمانده مورد استفاده قرار گرفت. بر اساس نتایج معیارهای ارزیابی ضریب تعیین R2، میانگین قدرمطلق خطا MAE، حداکثر خطا ME، میانگین مربعات خطا MSE، جذر میانگین مربعات خطا RMSE و جذر میانگین مربعات خطای نرمالشده NRMSE مدل بهینه انتخاب شد. نتایج تحقیق نشان داد که در تمام ایستگاهها به جز یک ایستگاه از بین مدلهای استفاده شده، مدل GB باتوجهبه معیارهای ارزیابی مدل عملکرد بهتری نسبت به مدل XGBoost برخوردار بوده است. همچنین نتایج نشان داد که برای صرفهجویی در زمان و هزینه و همچنین مدیریت بهینه ویژگیهای کیفیت آب، انتخاب سری شماره ۳ که در آن از سه ویژگی بهمنظور برآورد شاخص کیفیت آب (WQI) استفاده میشود، بهترین ترکیب بوده است. | ||
کلیدواژهها | ||
ویژگیهای کیفیت آب؛ رودخانه زایندهرود؛ مدلهای یادگیری ماشین؛ GB؛ XGBoost | ||
مراجع | ||
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