ترکیب روش‌ استخراج مشخصه با ریزمقیاس نمایی آماری مبتنی بر ترکیب مدل‌های هوش مصنوعی

رزاق زاده, زهرا; نورانی, وحید; بهفر, نازنین

doi:10.22060/ceej.2018.14986.5806

دانشگاه صنعتی امیرکبیر

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	ترکیب روش‌ استخراج مشخصه با ریزمقیاس نمایی آماری مبتنی بر ترکیب مدل‌های هوش مصنوعی
نشریه مهندسی عمران امیرکبیر
مقاله 6، دوره 52، شماره 4، تیر 1399، صفحه 841-858 اصل مقاله (1.95 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22060/ceej.2018.14986.5806
نویسندگان
زهرا رزاق زاده¹؛ وحید نورانی^* ²؛ نازنین بهفر²
¹عمران، دانشکده عمران، دانشگاه تبریز، تبریز، ایران
²مدیریت منابع آب، دانشکده عمران، دانشگاه تبریز، تبریز، ایران
چکیده
در این پژوهش از دو مدل گردش عمومی جو GCM) (ESM-BNU, ESM2-Can ))برای شبیه‌سازی بارش دوره آتی در شهر تبریز، استفاده شده است. مهم‌ترین ضعف مدل‌های GCM ،بزرگ بودن مقیاس مکانی متغیرهای اقلیمی شبیه‌سازی شده است که روش‌های مختلف ریزمقیاس نمایی درصدد رفع این نقیصه می‌باشند. در این مطالعه برای ریزمقیاس نمودن متغیرهای اقلیمی مدل‌های GCM ،از مدل‌های هوش مصنوعی شبکه عصبی مصنوعی ( ANN )و نروفازی ( ANFIS ،)بهره گرفته شده است. بدون شک اصلی‌ترین مرحله به هنگام استفاده از این مدل ها، انتخاب مناسب ترین ورودی از میان داده‌های بسیار متعدد ارائه شده توسط GCM ها می‌باشد. بنابراین در این مطالعه برای انتخاب پارامترهای ورودی مؤثر از روش های درخت تصمیم و تابع اطلاعات مشترک ( MI )استفاده شده است. هم چنین روش ترکیب مدل برای کاهش عدم قطعیت در ریزمقیاس نمایی و افزایش دقت پیش‌بینی استفاده شده است. در این پژوهش مقایسه نتایج روش‌های ریزمقیاس نمایی نشان داد که، مدل ترکیبی با موثرترین ورودی‌های تعیین شده با درخت تصمیم نتایج مناسب تری ارائه می‌دهد. به طوریکه در هر دو مدل GCM ،بهکارگیری مدل ترکیبی با پیش‌بینی کننده‌های مبتنی بر درخت تصمیم نسبت به مدل‌های ANN و ANFIS در ریزمقیاس نمای سبب افزایش %38%-10 DC در مدل‌سازی بارش می‌گردد. پیشبینی بارش ایستگاه سینوپتیک تبریز با مدل ترکیبی نشان داد که بارش دوره آتی (2060-2020 ) تحت سناریوهای 5.RCP4 و 5.RCP8 تا %40 %-30 کاهش می‌یابد..
کلیدواژه‌ها
مدل گردش عمومی جو؛ شبکه عصبی مصنوعی؛ نروفازی؛ تابع اطلاعات مشترک؛ ریزمقیاس نمایی آماری
موضوعات
مدیریت منابع آب
عنوان مقاله [English]
The conjunction of the feature extraction method with AI-based ensemble statistical downscaling models
نویسندگان [English]
zahra Razzaghzadeh¹؛ vahid nourani²؛ nazanin behfar²
¹civil engineer, faculty of civil engineering, Tabriz university, Tabriz, Iran
²water resource management, factuly of civil engineering, university of Tabriz, Tabriz,Iran
چکیده [English]
In this study, two general circulation models (GCMs) (Can-ESM2, BNU-ESM) were used to simulate the future precipitation of Tabriz city. The weakness of GCMs is the coarse resolution of climate variables in which the different methods of downscaling is about to solve this deficiency. In this study, the Artificial Intelligence (AI) models, i.e., Artificial Neural Network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS), were used to statistically downscale the climate variables of GCMs. Without any doubt, the most important step during the use of these models is selecting the dominant inputs among huge large-scale GCM data. So in this study for the selection of dominant inputs, decision tree, and mutual information (MI) feature extraction methods were used. Also, the ensemble techniques were used to evaluate the efficiency of downscaling models and to decrease the uncertainties. A comparison of the result of downscaling models indicated that the ensemble technique (i.e., hybrid of ANN and ANFIS) with dominant inputs based on decision tree feature extraction methods presents better performance. In both GCMs, the application of the downscaling ensemble couple with dominant predictors based on a decision tree model in precipitation downscaling showed 10%-38% increase in DC in versus the individual ANN and ANFIS downscaling models. The projection precipitation of Tabriz synoptic station for future (2020-2060) by proposed ensemble AI-based model indicated 30%-40% precipitation decreases under RCP4.5 and RCP8.5 scenarios.
کلیدواژه‌ها [English]
General Circulation Models (GCMs), Adaptive neuro-fuzzy inference system (ANFIS), Artificial Neural Network (ANN), Mutual Information (MI), Statistical Downscaling

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ترکیب روش‌ استخراج مشخصه با ریزمقیاس نمایی آماری مبتنی بر ترکیب مدل‌های هوش مصنوعی