نشریه علمی - پژوهشی مرتع و آبخیزداری

نشریه علمی - پژوهشی مرتع و آبخیزداری

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مدل‌سازی شاخص کیفیت آب (WQI) با استفاده از تکنیک‌های هوش مصنوعی (منطقه مورد مطالعه: بخشی از حوزه آبخیز کشکان)

نوع مقاله : مقاله پژوهشی

نویسندگان

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

10.22059/jrwm.2025.385428.1789

چکیده

این تحقیق با هدف مدل‌سازی شاخص کیفیت آب ( WQI) به کمک مدل‌های هوش‌مصنوعی با تکیه بر مدل‌های یادگیری ماشین RepTree، RF، M5P، BM5P(ترکیب M5P و Bagging)، BRF(ترکیب RF و Bagging) و BRepTree (ترکیب RepTree و Bagging) در حوضه‌های خرم‌آباد، الشتر و بیرانشهر، استان لرستان انجام گرفته است. در این تحقیق ابتدا بر اساس داده-های کیفیت آب، شاخص کیفیت آب (WQI) محاسبه شد و در ادامه برای مدل‌سازی، داده‌های ورودی شامل پارامترهای کیفی آب یک دوره 10 ساله (1403-1393) و همچنین داده خروجی شاخص کیفیت آب رودخانه‌ها بود. در این تحقیق برای مدل‌سازی در مرحله آموزش 70 درصد داده‌ها و در مرحله ارزیابی 30 درصد باقی‌مانده مورد استفاده قرار گرفتند و در نهایت بر اساس نتایج معیارهای ارزیابی خطای ضریب همبستگی (CC)، ریشه میانگین مربعات خطا (RMSE) و میانگین خطای مطلق (MAE) همچنین نمودارهای تیلور و ویولن باکس مدل بهینه انتخاب شد. نتایج تحقیق نشان داد که از بین مدل‌های استفاده شده، مدل BM5P باتوجه به معیارهای ارزیابی مدل CC، MAE و RMSE در بخش آزمایش به‌ترتیب برابر 99/0، 15/0 و 20/0 از عملکرد بهتری نسبت به سایر مدل‌ها برخوردار بوده است. در ادامه با توجه به نتایج حاصل از نمودارهای تیلور و ویولن باکس به‌منظور ارزیابی کارایی مدل‌ها، برتری این مدل نسبت به سایر مدل‌ها تایید شد. در نهایت نتایج نشان داد که برای صرفه‌جوی در زمان و هزینه و همچنین مدیریت بهینه پارامتر‌های کیفیت آب، یکی از راهکارهای مناسب، استفاده از تکنیک‌های هوش‌ مصنوعی به‌منظور مدل‌سازی و ارزیابی شاخص کیفیت آب (WQI) می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Water quality Index (WQI) modeling using Artificial Intelligence Techniques (AIT) (Case study: Kashkan watershed, Lorestan province)

نویسندگان [English]

  • Alireza Sepahvand
  • Nasrin Beiranvand
  • Negar Arjmand

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

چکیده [English]

Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and social progress and development. Therefore, temporal and spatial trending of water quality is necessary for planning water resource management. In this research, the performance of the six soft computing techniques, including, Random Forest, Reduced Error Pruning Tree (REPt), M5P model, bagging RF, bagging REPt and bagging M5P were compared to estimate the water quality index (WQI) in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province, Iran. At first, based on water quality data, water quality index (WQI) was calculated and ten distinct water quality parameters (2014 to 2023) were used as input variables and WQI as output. Total data set consists of water quality parameters of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models were compared with Correlation Coefficient (C.C.), Root Mean Square Error (RMSE), Maximum Absolute Error (MAE), Taylor diagram and Violin plot box. The obtained results suggest that the BM5P is more accurate to estimate the water quality index (WQI) compared to the M5P, ReepTree and Random Forest (RF) models for the given study area. According to the results of the test part of the BM5P model, it has given us the best result, which are the correlation coefficient, the Root Mean Square Error and the Mean Absolute Error 0.99, 0.2, and 0.15, respectively. Also, the Taylor diagram and violin box plot were concluded that BM5P was the most reliable soft computing technique for the prediction of WQI. Finally, the structure of Artificial Intelligence Techniques (AIT) for modeling is very simple and very less time consumable. Thus, the BM5P model can be useful in the water quality index (WQI) modeling not only for accuracy but also for its time-saving and simple structure compared with other models.

کلیدواژه‌ها [English]

  • Artificial Intelligence
  • Kharkheh watershed
  • Lorestan province
  • Modelling
  • Water Quality Index (WQI)
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نشریه علمی - پژوهشی مرتع و آبخیزداری
دوره 78، شماره 2
خرداد 1404
صفحه 207-223
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  • تاریخ دریافت: 25 آبان 1403
  • تاریخ بازنگری: 23 بهمن 1403
  • تاریخ پذیرش: 24 بهمن 1403
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