پیش‌بینی جریان ماهانه رودخانه با استفاده از رویکردهای آنتروپی شانون و موجک (مطالعه موردی: رودخانه مارون)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه هیدرولوژی و منابع آب ، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

2 دانشیار، گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

3 استادیار، گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

چکیده

جریان رودخانه از مهمترین اجزاء چرخه هیدرولوژی است که به عوامل اقلیمی متعددی وابسته بوده و برآورد دقیق آن در زمینه-های مختلف مدیریت منابع آب کاربرد دارد. در مطالعه حاضر از مدل‌های جنگل‌های تصادفی (RF) و ماشین بردار پشتیبان (SVM) برای پیش‌بینی جریان ماهانه رودخانه مارون در دوره آماری 1360 تا 1396 استفاده گردید. یکی از مراحل مهم در کاربرد مدل‌های هوش مصنوعی تعریف الگوهای ورودی و شناسایی پارامترهای موثر در فرآیند مدل‌سازی است. برای انتخاب بهینه‌ترین ورودی‌ها از بین بارش، تبخیر و دماهای کمینه، بیشینه و متوسط روش آنتروپی شانون استفاده شد. نتایج نشان داد که وزن بارش و تبخیر در مجموع بیش از 85 درصد بود. در گام بعد، سه ساختار متفاوت برای ورودی مدل‌ها توسعه داده شد. در حالت اول الگوهای اقلیم‌پایه تعریف شدند که از داده‌های هواشناسی به عنوان ورودی استفاده می‌کردند. در حالت دوم خاصیت تناوبی غیرخطی به الگوهای اقلیم‌پایه افزوده شد و در حالت سوم داده‌های ورودی اقلیم‌پایه با استفاده از پنج تابع موجک مادر تجزیه شده و مدل‌های هیبریدی W-RF و W-SVM ایجاد شدند. عملکرد مدل‌های منفرد RF و SVM نشان داد که با افزودن ترم پریودیک، دقت در مقایسه با ورودی‌های اقلیم پایه تا حدودی افزایش می‌یابد، اما تجزیه داده‌ها با تئوری موجک به طور قابل ملاحظه‌ای خطای مدل‌سازی را کاهش داد. در این بین عملکرد دو مدل W-RF و W-SVM بسیار نزدیک به یکدیگر بود، اما با توجه به نمودار ویلونی، مدل W-SVM به عنوان مناسب‌ترین گزینه برای پیش‌بینی جریان ماهانه رودخانه مارون پیشنهاد می‌گردد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of Monthly Streamflow Using Shannon Entropy and Wavelet Theory Approaches (Case study: Maroon River)

نویسندگان [English]

  • Mohammad Amin Nekoeeyan 1
  • Feridon Radmanesh 2
  • Farshad Ahmadi 3
1 M.Sc. Student, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Associate Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
3 Assistant Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده [English]

River flow is one of the most important components of the hydrological cycle, which depends on several climatic factors and its accurate estimation is used in various fields of water resources management. Therefore, in the present study, random forest (RF) and support vector machine (SVM) models were used to predict the monthly streamflow of the Maroon River in the period of 1981- 2017. One of the important steps in the application of artificial intelligence models is the definition of input patterns and determining the effective variables in the modeling process. The Shannon entropy method was used to select the most efficient inputs among precipitation, evaporation, and minimum, maximum, and average temperatures. The results showed that the total weight of precipitation and evaporation was more than 85 percent. In the next step, three different structures were developed for modeling. In the first case, climate-based patterns were defined that used meteorological data as input. In the second case, nonlinear periodicity was added to the climate-based patterns, and in the third case, the climate-based input data were decomposed using five mother wavelet functions, and W-RF and W-SVM hybrid models were created. The performance evaluation of the standalone RF and SVM models showed that by considering the periodic term, the accuracy is somewhat increased compared to the climate-based inputs, but the analysis of the data with wavelet theory significantly reduced the modeling error. In the meantime, the performance of the two models W-RF and W-SVM was very close to each other, but according to the violin plot, the W-SVM model is suggested as the most suitable option for predicting the monthly streamflow of the Maroon River.

کلیدواژه‌ها [English]

  • Climate based patterns
  • Decomposition level
  • Periodic term
  • Weighting

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مدیریت آب و آبیاری
دوره 12، شماره 1
فروردین 1401
صفحه 15-31

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