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پیش‌بینی رخداد بارش سنگین منطقه‌ای در جنوب غربی ایران با استفاده از متغیرهای همدیدی و روش‌های داده کاوی | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 2، اردیبهشت 1401، صفحه 317-332 اصل مقاله (1.24 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.338036.669197 | ||
| نویسندگان | ||
| کوکب شاهقلیان1؛ جواد بذرافشان* 1؛ پرویز ایران نژاد2 | ||
| 1گروه آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، تهران، ایران | ||
| 2دانشیار 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 | ||
| بارش سنگین منطقه‌ای؛ پیش بینی؛ داده کاوی؛ متغیرهای همدیدی؛ ایران | ||
| عنوان مقاله [English] | ||
| Prediction of Regional Heavy Precipitation Occurrence in the Southwest Iran Using Synoptic Variables and Data Mining Methods | ||
| نویسندگان [English] | ||
| Kokab Shahgholian1؛ Javad Bazrafshan1؛ Parviz Irannejad2 | ||
| 1Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran | ||
| 2Associate 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 | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 339 تعداد دریافت فایل اصل مقاله: 355 |
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