توسعه روش هیبریدی موجک-الگوریتم Kstar برای پیش‌بینی بارش‌های ماهانه (مطالعه موردی: ایستگاه سینوپتیک اهواز)

نوع مقاله : مقاله پژوهشی

نویسندگان

استادیار گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران

چکیده

پیش­بینی متغیرهای هیدرولوژیک و به ویژه بارش نقش بسیار مهمی در مدیریت و برنامه­ریزی منابع آبی داشته و از این­رو توسعه روش­هایی که بتواند تخمین دقیقی از آن را به دست دهد همواره مورد توجه محققان بوده است. در این پژوهش از داده­های بارش ایستگاه سینوپتیک اهواز در دوره آماری 2018-1961 برای توسعه مدل­های هیبریدی موجک Kstar (WKstar) و برنامه­ریزی بیان ژن (WGEP) استفاده شد. عملکرد مدل­های به کار رفته با شاخص­های آماری ضریب همبستگی (CC)، نش- ساتکلیف (NS)، کلینگ گوپتا (KGE) و ضریب ویلموت (WI) مورد بررسی قرار گرفت. در ابتدا مدل­های منفرد Kstar و GEP با ورودی­های بارش تاخیر یافته تا چهار ماه قبل و شماره ماه­ها اجرا شدند. نتایج نشان داد که هر دو مدل با تاخیر زمانی یک ماه (الگوی M1) به بیشترین دقت رسیده اما عملکرد آنها بسیار ضعیف و غیرقابل قبول بود. با توجه به اینکه هر دو مدل با الگوی M1 بهترین عملکرد را داشته­اند از این رو بارش­های یک ماه قبل با استفاده از پنج تابع موجک مختلف به زیرسری­های تقریب و جزئیات تجزیه شده و مجدداً به مدل­ها معرفی شدند. نتایج نشان داد که عملکرد مدل­های هیبریدی موجک نسبت به حالت منفرد بسیار بهبود یافته به طوری که شاخص NS از 139/0 به 607/0 افزایش یافت. همچنین بهترین عملکرد مدل­های هیبریدی WKstar و WGEP با ورودی­های تابع موجک دابچیز چهار و سطح تجزیه دو به دست آمده و از نظر آماری اختلاف معنی­داری بین دو مدل هیبریدی توسعه یافته وجود نداشت، اما با استفاده از نمودار ویولونی مشخص گردید که مدل WKstar برای پیش­بینی بارش­های ایستگاه سینوپتیک اهواز مناسب­تر می­باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Development of Wavelet-Kstar Algorithm Hybrid Model for the Monthly Precipitation Prediction (Case Study: Synoptic Station of Ahvaz)

نویسندگان [English]

  • Farshad Ahmadi
  • Mohammad Amin Maddah
Assistant Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده [English]

Predicting hydrological parameters, especially rainfall, has played a very important role in water resources management and planning. Therefore, the development of methods giving accurate estimates has always been of interest to researchers. In this study, precipitation data from the Ahvaz synoptic station in the period of 2018-1961 were used to develop Kstar and Gene Expression Programming wavelet hybrid models (WKstar and WGEP). The performance of the applied models was evaluated using statistical indices, including the correlation coefficient (CC), Nash-Sutcliffe (NS), Kling–Gupta (KGE) and the Willmott Index (WI). Initially, the Kstar and GEP individual models were implemented, with a delay in precipitation input up to four months ago and month numbers. Results showed that both models achieved maximum accuracy with a time delay of one month (M1 senario), but their performance was very low and unacceptable. Regarding that both models with the M1 pattern have the best performance, so the M1 inputs decomposed by five different wavelet functions to detail and approximat subsets and were represented to the models. The results showed that the performance of wavelet hybrid models was significantly improved, so that the NS index increased from 0.139 to 0.607. In addition, the best performance of WKstar and WGEP hybrid models was obtained with the inputs of the Daubechies wavelet of order four and the decomposition level two. Also, there was no significant difference between the two developed hybrid models statistically, but using the violin plot it was found that the WKstar model is more suitable for predicting precipitation at the Ahvaz synoptic station.

کلیدواژه‌ها [English]

  • Decomposition level
  • mother wavelet
  • Willmott index
  • Violin plot

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تحقیقات آب و خاک ایران
دوره 52، شماره 2
اردیبهشت 1400
صفحه 409-420

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