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ایرانشاهی, معین, ابراهیمی, بهروز, یوسفی, حسین, مریدی, علی. (1401). بررسی اثرات تغییر اقلیم بر وضعیت دما و بارش با استفاده از شبکه عصبی و گزارش ششم IPCC (مطالعه موردی: ایستگاه‎های الشتر و خرم‎آباد). , 12(4), 821-845. doi: 10.22059/jwim.2022.346796.1009
معین ایرانشاهی; بهروز ابراهیمی; حسین یوسفی; علی مریدی. "بررسی اثرات تغییر اقلیم بر وضعیت دما و بارش با استفاده از شبکه عصبی و گزارش ششم IPCC (مطالعه موردی: ایستگاه‎های الشتر و خرم‎آباد)". , 12, 4, 1401, 821-845. doi: 10.22059/jwim.2022.346796.1009
ایرانشاهی, معین, ابراهیمی, بهروز, یوسفی, حسین, مریدی, علی. (1401). 'بررسی اثرات تغییر اقلیم بر وضعیت دما و بارش با استفاده از شبکه عصبی و گزارش ششم IPCC (مطالعه موردی: ایستگاه‎های الشتر و خرم‎آباد)', , 12(4), pp. 821-845. doi: 10.22059/jwim.2022.346796.1009
ایرانشاهی, معین, ابراهیمی, بهروز, یوسفی, حسین, مریدی, علی. بررسی اثرات تغییر اقلیم بر وضعیت دما و بارش با استفاده از شبکه عصبی و گزارش ششم IPCC (مطالعه موردی: ایستگاه‎های الشتر و خرم‎آباد). , 1401; 12(4): 821-845. doi: 10.22059/jwim.2022.346796.1009

بررسی اثرات تغییر اقلیم بر وضعیت دما و بارش با استفاده از شبکه عصبی و گزارش ششم IPCC (مطالعه موردی: ایستگاه‎های الشتر و خرم‎آباد)

مدیریت آب و آبیاری
مقاله 9، دوره 12، شماره 4، بهمن 1401، صفحه 821-845    اصل مقاله (2.39 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22059/jwim.2022.346796.1009
نویسندگان
معین ایرانشاهی1؛ بهروز ابراهیمی2؛ حسین یوسفی3؛ علی مریدی* 3
1گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران.
2شرکت آب منطقه‌ای استان لرستان، ایران.
3گروه مهندسی منابع آب، دانشکده عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران.
چکیده
هدف از این پژوهش ارزیابی اثرات تغییر اقلیم بر دما، بارش و خشکسالی‌های آینده در ایستگاه‌های الشتر و خرم‌آباد می‌باشد، ‏که به این منظور از خروجی مدل گردش عمومی ‏MRI-ESM2‎‏ مطابق جدیدترین گزارش‎ IPCC‏(گزارش ششم)‏‎ ‎و سناریوهای ‏انتشار ‏SSP 1.2.6 ‎، ‏SSP 2.4.5‎، ‏SSP 3.7.0 ‎‏ و ‏‎ SSP 5.8.5‎‏در این مناطق مطالعاتی استفاده شد. با کاربرد مدل درخت تصمیم ‏‏(‏‎M5 Tree‎)، غالب‌ترین متغیرهای پیشبینی‌کننده مدل ‏MRI-ESM2‎‏ انتخاب شدند. در ادامه متغیرهای پیشبینی‌کننده به‌عنوان ‏ورودی در مدل ریزمقیاس‌نمایی آماری شبکه عصبی مصنوعی پیشرو‎ ‎‏(‏FFNN‏) قرار گرفتند و با الگوریتم بهینه‌سازی کرم‌شب‌تاب ‏‏(‏FFA‏)، فرایند‎ ‎ریزمقیاس نمایی پارامترهای بارش، دمای حداکثر و دمای حداقل برای دوره پایه (2014-1970) با نتایج مطلوب ‏انجام شد. پس از اثبات قابلیت مدل شبکه عصبی، پیشبینی تغییرات متوسط دما و بارش ماهانه در طی دوره‌های آینده نزدیک ‏‏(2062-2023) و آینده دور (2100-2063) تحت سناریوهای خط سیرهای مشترک اجتماعی-اقتصادی (‏SSP‏) مربوط به مدل ‏جفت‌شده فاز ششم (‎CMIP6‎‏) به انجام رسید. به‌طورکلی، نتایج نشان داد که این متغیرها در هر دو دوره آتی در مقیاس ماهانه ‏دارای نوسان‌های متعددی خواهند بود، به‌طوری‌که در دو ایستگاه الشتر و خرم‎آباد طی دوره‌های آینده نزدیک و آینده دور دمای ‏حداکثر و دمای حداقل نسبت به دوره مشاهداتی در تمام سناریوهای ‏SSP‏ روندی افزایشی خواهند داشت و تغییرات دمای حداقل ‏نسبت به دمای حداکثر بیش‌تر خواهد بود. در آینده نزدیک میانگین بارش سالانه ایستگاه الشتر بین سه دهم تا 16 درصد و ‏ایستگاه خرم‌آباد بین هفت تا 12 درصد تحت سناریوهای ‏SSP‏ کاهش خواهد داشت. در آینده دور نیز میانگین بارش سالانه ‏ایستگاه الشتر بین 10 تا 20 درصد و ایستگاه خرم‌آباد بین 12 تا 24 درصد تحت سناریوهای ‏SSP‏ کاهش خواهد داشت.‏
کلیدواژه‌ها
CMIP6؛ FFA؛ FFNN؛ SSP
عنوان مقاله [English]
Investigating the Effects of Climate Change on Temperature and Precipitation Using Neural Network and CMIP6 (Case Study: Aleshtar and Khorramabad Stations)
نویسندگان [English]
Moein Iranshahi1؛ Behrouz Ebrahimi2؛ Hossein Yousefi3؛ Ali Moridi3
1Department of Water Science and Engineering, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran.
2Planning Deputy of the Regional Water Company of Lorestan Province, Iran.
3Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran,
چکیده [English]
The purpose of this research is to evaluate the effects of climate change on temperature, precipitation, and future droughts in Al-Shatar and Khorramabad stations, for this purpose, the output of the general circulation model MRI-ESM2 according to the latest IPCC report (sixth report) and the emission scenarios SSP 1.2.6, SSP 2.4 5, SSP 3.7.0 and SSP 5.8.5 were used in these study areas. Using the decision tree model (M5 Tree), the most dominant predictor variables of the MRI-ESM2 model were selected. Next, the predictor variables were included as input in the advanced artificial neural network (FFNN) statistical microscale model and with the firefly optimization algorithm (FFA), the process The exponential micro-scale of precipitation parameters, maximum temperature, and minimum temperature for the base period (1970-2014) was carried out with favorable results in the studied stations. After proving the capability of the neural network model, forecasting the average temperature and monthly precipitation changes during the near future periods (2062 2023-2023) and the distant future (2063-2100) were carried out under the scenarios of the joint socio-economic trajectories (SSP) related to the coupled model of the sixth phase (CMIP6). In general, the results showed that these variables in both future periods On a monthly scale will have several fluctuations, so that in the two stations of Aleshtar and Khorramabad, during the periods of the near future and the distant future, the maximum temperature, and the minimum temperature will have an increasing trend compared to the observation period in all SSP scenarios, and the minimum temperature changes compared to The maximum temperature will be higher in the near future The annual rainfall of Elshtar station will decrease between 0.3 Percent and 16 Percent and Khorramabad station between Seven percent and 12 Percent under SSP scenarios. In the distant future, the average annual precipitation of Aleshatar station will decrease between 10-20 Percent and Khorramabad station between 12-24% under SSP scenarios.
کلیدواژه‌ها [English]
CMIP6, FFA, FFNN, SSP
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