بررسی عملکرد رویکردهای مختلف هوش مصنوعی در ریزمقیاس‌نمایی دما (منطقه مورد مطالعه: استان اردبیل)

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه انرژی های نو و محیط زیست، دانشکده علوم و فنون نوین، دانشگاه تهران، تهران، ایران

چکیده

سابقه و هدف: پیش بینی بلند و کوتاه مدت آب و هوا از جمله چالش­ های بسیار مهم محققان آب و اقلیم  بوده است. به منظور فائق آمدن بر این چالش، ابزارهای متعددی از جمله مدل­ های گردش عمومی جوی-اقیانوسی، سناریوهای پیش بینی و مدل­ های ریزمقیاس نمایی توسعه و استفاده شده است. این ابزارها با ایجاد همبستگی بین داده­ های بزرگ مقیاس مدل­ های گردش عمومی و داده ­های کوچک مقیاس سینوپتیک، به ریزمقیاس نمایی سناریوهای پیش ­بینی می­ کنند.
مواد و روش ­ها: در این مطالعه پارامترهای پیش ­بینی­ کننده بزرگ مقیاس دوره آماری 1961 تا 2003 از پایگاه داده مراکز ملی پیش بینی محیط زیست (NCEP) ، داده ­های بزگ مقیاس سناریوهای پیش ­بینی A1B و A2 مدل HadCM3 دوره آماری 2001 تا 2100 از مرکز  ارزیابی و مدلسازی اقلیم کانادا موسوم به CCCma، و داده ­های سینوپتیک هواشناسی ایستگاه ­های اردبیل از سازمان هواشناسی دریافت شده است. در این مطالعه سه روش (SDSM)، حداقل مربعات ماشین بردار پشتیبان (LS-SVM) و پرسپترون چند لایه (MLP) برای ریزمقیاس نمایی، و از سنجنده ­های آماری CC، MSE، RMSE، NMSE، Nash-Sutcliffe، MAE و دیاگرام تیلور به منظور ارزیابی کارایی روش ­های ریز مقیاس نمایی مورد استفاده قرار گرفت.
نتایج و بحث: نتایج نشان داده است که مدل MLP بر اساس میانگین ایستگاه ­ها بهترین نتیجه را با مقادیر (CC=0/85)، (NMSE=0/63)، (NSH=0/73) و (MAE=0/52) کسب کرده و در رتبه ­های دوم و سوم به ترتیب مدل ­های LS-SVM و SDSM قرار گرفته اند. دیاگرام تیلور نیز مدل SDSM را ضعیف ترین مدل شناسایی و نتایج دو مدل LS-SVM و MLP را با اختلاف کمی با یکدیگر به عنوان مدل­ های برتر معرفی کرد. بر اساس نتایج ریزمقیاس نمایی، تمامی سناریوهای پیش بینی افزایش دمای میانگین روزانه تا سال 2100 در تمامی ایستگاه ­های مورد مطالعه را پیش بینی کرده اند.
نتیجه گیری: بر اساس نتایج مطالعه، در تمامی سناریوهای پیش بینی و بر اساس تمامی روش ­های ریز مقیاس نمایی، افزایش دمای میانگین روزانه پیش بینی شده است، لذا لازم است تا در تهیه سیاست­ های توسعه ­ای و محیط زیستی این موضوع در نظر گرفته بشود.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluating the performance of artificial intelligence models for temperature downscaling (Study area: Ardabil province)

نویسندگان [English]

  • Mohammad Hossein Jahangir
  • Seyed Mohammad Ehsan Azimi
Department of Renewable Energies and Environment, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
چکیده [English]

Introduction: Long and short term weather forecasting have been as two of the most important challenges to water and climate researchers. In order to overcome this challenge, several tools, including atmospheric-ocean general circulation model forecasting scenarios, and downscaling models have been developed and used. These tools downscale forecasting scenarios by creating relationship between parameters of synoptic stations and Large-scale data of general circulation models.
Material and methods: In this study large-scale predictor parameters from 1961 to 2003 from the database of National Centers for Environmental Prediction (NCEP), large-scale data for the A1B and A2 forecast scenarios of the HadCM3 model from 2001 to 2100 from the Canadian Centre for Climate Modelling and Analysis (CCCMA), and the meteorological synoptic data of Ardabil stations from the Meteorological Organization were gathered. To this end, three downscaling models such as Statistical Downscaling Model (SDSM), least squares support vector machine (LS-SVM) and multi-layer perceptron (MLP) were determined for downscaling; and correlation coefficient (CC), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Normalized mean square error (NMSE), Nash-Sutcliffe, Mean Absolute Error (MAE), and Taylor diagram were used to evaluate the efficiency of the models.
Results and discussion: The results showed that the MLP obtained the best results based on the average of the stations with the values of (CC=0.85), (NMSE=0.63), (NSH=0.73) and (MAE=0.52), and LS-SVM and SDSM are ranked second and third, respectively. Taylor's diagram also identified the SDSM as the weakest and the LS-SVM and MLP as superior models with a slight difference. Based on downscaling results of all forecasting scenarios, an increase in average daily temperature is also predicted by 2100 in all studied stations.
Conclusion: Based on the results of the study, all forecasting scenarios and all methods of downscaling show increasing the daily average temperature by 2100. Hence, it is necessary to be taken this issue account for making environmental and developing policies in this area.

کلیدواژه‌ها [English]

  • MLP
  • LS-SVM
  • SDSM
  • Downscaling
  • Average daily temperature
  • Climate change scenarios
  • Ardabil province

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فصلنامه علوم محیطی
دوره 20، شماره 4
1401
صفحه 243-258

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