مدل‌سازی رسوب انحلالی با استفاده از الگوریتم‌های یادگیری ماشین در دوره‌های کم‌آبی و پرآبی (مطالعة موردی: حوضه‌های آبخیز خرم‌آباد، بیرانشهر و الشتر، استان لرستان)

بیرانوند, نسرین; سپه وند, علیرضا; حقی‌زاده, علی

doi:10.22059/jrwm.2023.351372.1684

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، لرستان، ایران

10.22059/jrwm.2023.351372.1684

چکیده

این تحقیق با استفاده از الگوریتم‌های یادگیری ماشین به بررسی کارآیی مدل‌های RF, RepTree, GP-PUK, GP-RBF, M5P برای مدل‌سازی بارانحلالی در زیرحوضه‌های خرم‌آباد، بیرانشهر و الشتر در استان لرستان پرداخته شد. داده‌های ورودی شامل بارش، دبی، دبی یک روز قبل، میانگین دبی (دبی همان روز و یک روز قبل) همچنین داده خروجی رسوب انحلالی رودخانه‌ها می‌باشد. در این تحقیق برای مدل‌سازی در مرحله آموزش 70 درصد داده‌ها و در مرحله آزمایش 30 درصد باقی‌مانده مورد استفاده قرار گرفتند. در نهایت برای مقایسه نتایج مدل‌های مختلف و انتخاب بهترین مدل، از معیارهای سنجش خطای ریشه میانگین مربعات خطا (RMSE)، ضریب همبستگی (C.C) و میانگین مربعات خطا (MAE) استفاده شد. نتایج نشان داد باتوجه به معیارهای ارزیابی مدل GP با دو تابع کرنل PUK و RBF در دوره‌‌ پرآبی و کم‌آبی عملکرد بهتری را نسبت به سایر مدل‌ها داشته است. نتایج به‌دست آمده در دوره پرآبی نشان داد که در ایستگاه‌های چم‌انجیر، سراب صیدعلی و کاکارضا مدل GP-RBF و در ایستگاه هیدرومتری بهرام‌جو مدل GP-PUK با بیشترین ضریب همبستگی و کمترین خطا در مرحله آزمایش به‌عنوان مدل‌های بهینه برای تخمین بار انحلالی انتخاب شدند. همچنین در ایستگاه‌های هیدرومتری بهرام‌جو، چم‌انجیر و سراب صیدعلی مدل GP-RBF و در ایستگاه هیدرومتری کاکارضا مدل GP-PUK به‌عنوان مدل بهینه برای تخمین بار انحلالی در دوره کم‌آبی انتخاب شدند. بنابراین، با توجه به نتایج به دست آمده، می‌توان برای مدیریت کیفیت و کمیت منابع آب سطحی از مدل‌های بهینه GP-PUK و GP-RBF برای تخمین بار انحلالی رودخانه‌های فاقد ایستگاه هیدرومتری در حوضه‌های کارستی استفاده کرد.

کلیدواژه‌ها

عنوان مقاله [English]

Total Dissolved Solids modeling using machine learning algorithms in periods of low and high water (Case study: Khorammabad, Biranshahr and Alashtar watersheds, Lorestan province)

نویسندگان [English]

Nasrin Beiranvand
Alireza Sepahvand
Ali Haghizadeh

Department of Range and Watershed Management, Faculty of Natural Resources, Lorestan University, Lorestan, Iran.

چکیده [English]

In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge and solute load (SL) of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE). Results suggest that GP-PUK and GP-RBF models works well than other modeling approaches in estimating the SL in low and high water-periods. The result showed that, In the high-water period, in Cham Anjir, Sarab Said Ali and Kaka Reza stations the GP-RBF model and in the Bahram Joo station the GP-PUK model with the highest C.C and the lowest error were selected the optimal models in estimating the SL. Also, in the low water period, result shown that in Cham Anjir, Sarab Said Ali and Bahram Joo stations the GP-RBF model and in the Kaka Reza station the GP-PUK model were the best models in estimating the SL. Therefore, these models can be used to estimate the solute load of nearby rivers by/without hydrometry station for the management of the quantity and quality of surface water.

کلیدواژه‌ها [English]

Lorestan province
Kashkan Watershed
Total Dissolved Solids (TDS)
Flow Duration Curve (FDC)
Gaussian process
Random Forest

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نشریه علمی - پژوهشی مرتع و آبخیزداری

مراجع

مراجع

دوره 76، شماره 3
آبان 1402
صفحه 215-236

مراجع

مراجع

دوره 76، شماره 3آبان 1402صفحه 215-236

دوره 76، شماره 3
آبان 1402
صفحه 215-236