معرفی یک مدل غیر‌خطی بر اساس هیبرید ماشین‌های یادگیری به منظور مدل‌سازی و پیش‌بینی بارش و مقایسه با روش SDSM (مطالعات موردی: شهرکرد، بارز و یاسوج)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد، گروه مهندسی و مدیریت منابع آّب، دانشکده مهندسی عمران، دانشگاه سمنان

2 گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه سمنان

3 استادیار گروه مهندسی آب و سازه‌های هیدرولیکی، دانشکده مهدسی عمران، دانشگاه سمنان

چکیده

در پژوهش حاضر، مدلی هیبریدی بر مبنای روش­های غیرخطی شامل رگرسیون تطبیقی چندگانه اسپلاین (MARS)، شبکه‌عصبی مصنوعی (ANN) و K نزدیکترین همسایه (KNN) به منظور ریز­مقیاس­نمایی و پیش­بینی بارش ایستگاه‌های شهرکرد، بارز و یاسوج تحت شرایط تغییر اقلیم معرفی شده است. مدل هیبریدی ارائه شده، مانند مدل ریز­مقیاس‌نمایی SDSM، از دو گام طبقه­بندی و رگرسیون تشکیل شده است. مدل MARS برای طبقه­بندی وقوع بارش و الگوریتم­های ANN و KNN برای تعیین مقدار بارش به­کار برده شده­اند. نتایج مدل MARS برای تعیین وقوع بارش نشان می­دهد که مدل مذکور نسبت به مدل SDSM از دقت بیش­تری برخوردار است. با مقایسه نتایج ریز­مقیاس­نمایی مشاهده می­شود که الگوریتم ANN نسبت به مدل SDSM و الگوریتم KNN دارای دقت بیش­تری در تعیین میانگین سالانه و ماهانه بارش است. به­طوری که در ایستگاه شهرکرد مقدار معیار R برای الگوریتم ANN نسبت به مدل SDSM به اندازه 54 درصد دقیق­تر است. هم­چنین، الگوریتم­های ANN، KNN و SDSM از نظر بیش­ترین دقت در سه ایستگاه بررسی شده، با در نظر گرفتن میانگین، انحراف معیار و ضریب چولگی ماهانه به ترتیب در رتبه­های اول، دوم و سوم قرار داده می­شوند. در نهایت، مقدار تغییرات بارش در دوره آینده نزدیک (2020-2040) و آینده دور (2070-2100) تحت سناریو­های A2 و B2 مدل HADCM3 بررسی شد. نتایج نشان داد که کم­ترین کاهش بارش (2 درصد) مربوط به الگوریتم ANN (در ایستگاه شهرکرد) و سناریوی A2 در دوره آینده نزدیک و بیش­ترین آن (54 درصد) مربوط به مدل SDSM (در ایستگاه یاسوج) و سناریوی A2 در دوره آینده دور می­باشد. در نهایت می‌توان نتیجه گرفت که هیبرید ماشین‌های یادگیری نسبت به مدل SDSM، از دقت بیشتری برخوردار است و می‌توان از مدل معرفی شده به عنوان جایگزین مدل SDSM استفاده کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Introducing a Nonlinear Model Based on Hybrid Machine Learning for Modeling and Prediction of Precipitation and Comparison with SDSM Method (Cases Studies: Shahrekord, Barez, and Yasuj)

نویسندگان [English]

  • Mahdi Valikhan Anaraki 1
  • Sayed-Farhad Mousavi 2
  • Saeed Farzin 3
  • hojat karami 3
1 Graduated MSc., Department of Water Resources Engineering and Management, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
2 Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University
3 Assistant Professor, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan Univercity, Semnan, Iran.
چکیده [English]

In the present study, a nonlinear hybrid model, based on multivariate adaptive regression splines (MARS), artificial neural networks (ANN) and K-nearest neighbor (KNN) has been presented for downscaling the precipitation of Shahrekord, Barez, and Yasuj under climate change conditions. This model, similar to SDSM, is composed of two steps; classification and regression. The MARS model is employed for classification of precipitation occurrence and the ANN and KNN are employed for determination of the amount of precipitation. The results of MARS showed that the mentioned model is more accurate than the SDSM model. Comparing the results of downscaled precipitation showed that the ANN model is more accurate than the SDSM and KNN in prediction of average annual and monthly precipitation. So that the R value for ANN was 54% more than the one in SDSM model, in Shahrekord. Also, according to the highest accuracy, standard deviation and skewness coefficient, the ANN, KNN and SDSM model ranked first, second, and third, respectively, for prediction of monthly average precipitation in three investigated stations. Eventually, the precipitation changes in the near future (2020-2040) and far future (2070-2100) periods were investigated under the A2 and B2 scenarios of the HADCM3 model. Results revealed that the lowest precipitation reduction is corresponded to ANN (in Shahrekord) and A2 scenario in the near future period and the highest precipitation reduction is corresponded to SDSM (in Yasuj) and A2 scenario in the far future period. Finally, it can be concluded that the proposed model is more accurate than the SDSM model and can be used as an alternative to the SDSM model.

کلیدواژه‌ها [English]

  • climate change
  • Downscaling
  • Machine learning
  • Precipitation
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