پیش‌بینی بلندمدت بارش بر پایه الگوهای پیوند دور اقلیمی، مطالعه موردی: حوضه آبریز اهرچای

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، دانشکده مهندسی عمران، دانشگاه صنعتی اصفهان

2 استادیار، دانشکده مهندسی آب، پردیس فنی مهندسی شهید عباسپور، دانشگاه شهید بهشتی، تهران

3 دانشجوی دکترای علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران

4 دانش‌آموخته کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه صنعتی اصفهان

چکیده

هدف از این پژوهش، پیش‌بینی‌ بارش فصلی حوضه آبریز اهرچای در شمال غربی ایران بود که با استفاده از الگوهای پیوند دور اقلیمی شامل اطلاعات متوسط فشار هوا و دما در سطح دریاها در طول دوره آماری 1965 تا 2005 به‌دست آمد. مدل‌های پیش‌بینی برای دو فصل تر (از دسامبر تا می) و فصل خشک (از ژوئن تا نوامبر) توسعه داده شدند. از این رو، پس از دریافت اطلاعات الگوهای پیوند دور اقلیمی‌ شناسایی شده بر اقلیم شمال غرب کشور، از روش‌ همبستگی و روش آزمون گاما برای انتخاب بهترین متغیرهای پیش‌بینی کننده و بهترین ترکیب آنها استفاده شد. در نهایت با استفاده از مدل ماشین‌ بردار پشتیبان، بارش فصلی پیش‌بینی و نتایج آن با مدل رگرسیون چند متغیره مقایسه شد. نتایج نشان‌دهنده نقش مؤثر مدل آزمون گاما در تعیین متغیرهای ورودی و ترکیب آنها بود. همچنین عملکرد مدل ماشین بردار پشتیبان با مدل مبنای رگرسیون چند متغیره به‌عنوان یک مدل مبنا مقایسه شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Long-Lead Rainfall Prediction Based on Climate Patterns of Tele-Connection, A Case Study: Aharchay Basin

نویسندگان [English]

  • azadeh Ahmadi 1
  • Ali Moridi 2
  • Elham Kakaei Lafdani 3
  • Ghasem Kiyanpisheh 4
چکیده [English]

This study aims to develop a seasonal rainfall prediction model for the Aharchay Basin, northwest of Iran. The model is based on climate patterns of tele-connection including sea level pressure (SLP) and sea surface temperature (SST) over the period from 1965 to 2005. The models cover both wet (from December to May) and dry (from June to November) seasons. For this purpose, the climatic patterns affecting the climate of the northwest of Iran were initially determined. In the second stage of the study, the correlation coefficient analysis and the Gamma Test (GT) technique were used to select the best predictors and to determine the best combination of the variables. The results revealed that the gamma test model outperformed the other model in determining the required input variables and their best combination. The seasonal rainfall in the basin was also predicted using the Support Vector Machines (SVM) and the results thus obtained were compared with those of the multivariate linear regression model as a benchmark to show the performance of the SVM model in rainfall prediction.

کلیدواژه‌ها [English]

  • Rainfall Prediction
  • Climate Patterns of Tele-Connection
  • Support Vector Machines
  • Gamma Test
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