پیش‌بینی آورد با روش‌های برنامه‌ریزی بیان ژن و وایازی درخت تصمیم با دیدگاه تغییر اقلیم در آبخیز کنگاور استان کرمانشاه

نوع مقاله : پژوهشی

نویسندگان

1 استادیار گروه علوم و مهندسی آب، دانشگاه رازی

2 دانشجوی کارشناسی ارشد منابع آب، دانشگاه رازی

چکیده

تغییر اقلیم، گرم‌تر شدن زمین، و مدیریت نامناسب اندوخته‌های آب از جمله دشواری‌هایی است که موجب نگرانی جامعه‌ی بشری به‌ویژه در مدیریت آبخیزها شده‌ ‌‌‌‌‌است. محققان و مهندسان برای جلوگیری از هدر‌رفت، و بهینه‌سازی اندوخته‌های آب در این حوزه‌ها اقلیم را پیش‌بینی می‌کنند. در این پژوهش ایستگاه آران در آبخیز کنگاور برای ارزیابی تغییر بارش، دما، و آورد در آینده با در نظر‌گرفتن حالت‌های ممکن اقلیمی گزارش پنجم (CMIP5) در نظر گرفته ‌شده‌ است. با دردست داشتن اطلاعات مشاهده‌یی در 33 سال از 1983 تا 2015 از روش‌های برنامه‌ریزی بیان ژن و درخت تصمیم برای آموزش، آزمون، و پیش‌بینی داده‌ها بهره گرفته شد. از میان مدل‌های گوناگون اقلیمی مدل Fgoals-g2 به دلیل مشابهت بیش‌تر ویژگی‌های آماری داده‌های تاریخی با داده‌های بارش و دمای منطقه برای پیش‌بینی در دوره‌ی‌ 2052- 2020 برگزیده ‌شد. از حالت ممکن Rcp2.6 حالت خوش‌بینانه، و روش ریزمقیاس‌گردانی عامل‌های تغییر، و ورودی‌های مدل‌ GEP و نرم‌افزارORANGE  برای پیش‌بینی ویژگی آورد در آینده بهره‌‌‌ گرفته ‌شد. بیشینه‌ی دمای ماهانه به °C 31/18 می‌رسد. بیشینه‌ی میانگین بارش ماهانه در دوره‌ی آینده نسبت به دوره‌ی پایه حدود 4 % افزایش خواهد یافت، و به 169/51 میلی‌متر می‌رسد. مقدار میانگین سالانه‌ی درازمدت بارش از 423/39 میلی‌متر به 427/ 15 میلی‌متر افزایش خواهد یافت. ضریب هم‌بستگی برای داده‌های آزمون در روش برنامه‌ریزی بیان ژن 0/70 بود و مقدار میانگین آورد از m3/s 3/33 به  m3/s 3/14 تغییر می‌کند، که نشان می‌دهد 71/5 % کاهش خواهد یافت. در روش وایازی درخت تصمیم ضریب هم‌بستگی 0/995 بود و میانگین آورد 10/3 % افزایش خواهد یافت و به 3/69 متر مکعب بر ثانیه خواهد رسید.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of the Discharge Rate of the Kangavar Watershed, the Province of Kermanshah, Using Gene Expression Programing and the Decision Tree Regression

نویسندگان [English]

  • Maryam Hafezparast Mavadat 1
  • Foroozan Payfeshorde 2
1 Ph.D. in Water Resources Engineering, Assistant Professor, Department of Water Science and Engineering, Razi University
2 M.Sc. Student of Water Resources Engineering, Razi University
چکیده [English]

A lack of sufficient water resources, climate change, and especially global warming, is causing apprehension in societies, particularly in watershed managers. Scientists and engineers are forecasting climatic data to prevent the waste of water resources and to optimize their use in watersheds. The Aran Station in the Kangavar Watershed was chosen to predict the trend of temperature, precipitation, and runoff using the CMIP5 climate model. Benefiting from 33 years of data (1983 to 2015), the Gene Expression Programming method (GEPM) and Decision Tree methods were developed to train, test, and predict the river discharge rate. Different climate models were implemented using the historical data of the study area. The Fgoals-g2 was chosen to predict temperature and precipitation data for the 2020-2052 periods. The RCP2.5 climate scenario was used as an optimistic scenario, and the output of the change factor downscaling method was used as an input for the GEP model and the ORANGE Software to find the best prediction of the discharge parameter in the future. The results indicated that the temperature of the next cycle will increase by 13 degrees and the maximum monthly temperature will reach 31.18 degrees centigrade. The maximum monthly precipitation will increase by 4 percent and reach 169.51mm. The longtime yearly mean precipitation will change from 423.39 mm to 427.15 mm. The correlation coefficient of the test data in the GEPM was 0.70. The maximum monthly discharge will decrease 1.84 percent, from 29.31 to 28.77 cubic meters per second (m3s-1). The mean discharge will decrease 5.71 percent, from 3.33 to 3.14 cubic meters per second. The correlation coefficient of the test data in the decision tree regression method, using the ORANGE software was 0.995. The mean discharge will increase by 10 percent and reach 3.69 m3s-1. The maximum yearly discharge will decrease by 6 percent, from 7.62 to 7.12 m3s-1.

کلیدواژه‌ها [English]

  • Gene expression programing
  • orange
  • CMIP5 models
  • rainfall- runoff

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  • تاریخ دریافت: 01 اردیبهشت 1400
  • تاریخ بازنگری: 26 خرداد 1400
  • تاریخ پذیرش: 30 خرداد 1400

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