پیش‏بینی رخداد بارش‏ سنگین منطقه‌ای در جنوب غربی ایران با استفاده از متغیرهای همدیدی و روش‌های داده‏ کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، تهران، ایران

2 دانشیار گروه فیزیک فضا-موسسه ژئوفیزیک دانشگاه تهران

چکیده

پیش‌بینی‌ کوتاه‌مدت بارش‏های سنگین اهمیت ویژه‏ای در هشدار سیل و به‌حداقل‌رساندن آسیب‏های ناشی از آن دارد. در این مطالعه، تعریف جدیدی از بارش سنگین منطقه‌ای برپایه الگوی احتمالاتی رگبارها ارائه شد. برای این منظور از داده‌های ‌بارش روزانه (2018-1987) مربوط به 12 ایستگاه همدید در جنوب غرب ایران استفاده شد. به‌علاوه، شش متغیر همدیدی در ترازهای 1000 تا 200 هکتوپاسکال مربوط به یک تا پنج روز قبل از بارش سنگین (که گستره وسیعی در خارج منطقه مطالعاتی را پوشش می‌دهند) به‌عنوان پیش‌بینی گر مورداستفاده قرار گرفت. برای اجرای این پژوهش از چهار روش انتخاب متغیر و ده مدل یادگیری ماشین از نوع طبقه‌بندی‌کننده دودوئی استفاده شد. نتایج نشان داد که به‌منظور تشخیص بارش‏های سنگین از غیر سنگین، بهترین حالت استفاده از داده‏های تا چهار روز پیش از رخداد بارش است. همچنین، از بین چهار روش انتخاب متغیر، روش‏های Chi-Square و Extra Tree برCorrelation  و Random Forest  برتری دارند. در نتیجه این مطالعه مشخص شد که مدل Random Forest با روش انتخاب متغیر Chi-Square بالاترین کارایی در پیش‌بینی بارش‏های سنگین در منطقه مطالعاتی را دارد. متغیرهای همدیدی مناسب برای پیش‌بینی بارش سنگین شامل رطوبت نسبی و رطوبت ویژه 1-2 روز قبل و باد برداری 2-4 روز قبل از رخداد بودند.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of Regional Heavy Precipitation Occurrence in the Southwest Iran Using Synoptic Variables and Data Mining Methods

نویسندگان [English]

  • Kokab Shahgholian 1
  • Javad Bazrafshan 1
  • Parviz Irannejad 2
1 Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran
2 Associate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran
چکیده [English]

Short-term prediction of heavy precipitation events is especially crucial in flood warning and mitigation. This study offered a novel concept of the regional heavy precipitation based on the probability pattern of a typical rainstorm. Daily precipitation data of 12 synoptic stations located over southwestern Iran were used for this purpose. In addition, six synoptic variables at 1000 to 200 hPa pressure levels on one to five days before heavy precipitations (covering a wide range outside the study area) were used as predictors. All data used in this study cover the period 1987- 2018. Four feature selection methods and 10 binary classifier machine-learning models were employed in this study. The results revealed that using synoptic data up to four days prior to the events best distinguishes heavy precipitation from non-heavy precipitation events. In addition, among the four feature selection methods, Chi-Square and Extra Tree methods are superior to Correlation and Random Forest. As a result of this study, it was found that the Random Forest model with the Chi-Square feature selection method has the highest efficiency in predicting regional heavy precipitation events in the study area. Relative humidity and specific humidity 1-2 days before and wind speed 2-4 days before the precipitation events are relevant synoptic variables for predicting heavy precipitation events.

کلیدواژه‌ها [English]

  • regional heavy precipitation
  • Prediction
  • Data Mining
  • Synoptic variables
  • Iran
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