اخبار و اعلانات
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| تعداد مقالات | 60,167 |
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توسعۀ مدل ناپارامتری شبیه‌ساز داده‌های ماهانۀ هیدرولوژیکی | ||
| مدیریت آب و آبیاری | ||
| مقاله 6، دوره 3، شماره 1، شهریور 1392، صفحه 83-95 اصل مقاله (1.14 MB) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jwim.2013.35739 | ||
| نویسندگان | ||
| سلمان شریف آذری1؛ شهاب عراقی نژاد2 | ||
| 1مربی گروه مهندسی آب، دانشکدة آب و خاک، دانشگاه زابل، زابل - ایران | ||
| 2استادیار گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج - ایران | ||
| کلیدواژه‌ها | ||
| چندمتغیره؛ خودگردان‌ساز نزدیک‌ترین همسایه؛ رودخانۀ مهاباد؛ سری‌های زمانی؛ مدل ناپارامتری | ||
| عنوان مقاله [English] | ||
| Develop a non-parametric model to simulate monthly hydrological data | ||
| نویسندگان [English] | ||
| Salman Sharifazari1؛ Shahab Araghinejad2 | ||
| چکیده [English] | ||
| One of the water resources modeling requirements is sufficient knowledge of long-term series of meteorological and hydrological parameters. In this study the nearest neighbor resampling method presented by Lall and Sharma was developed. In the developed model, the KNN regression was used for time series forecasting instead of local polynomial used in the developed algorithm by Prairie. In this case problems caused by polynomial degree estimation were solved. This caused that multivariate time series simulation became feasible. To evaluate the performance of the developed model recorded data in the Kawtar hydrometer station located on one of the main branch of Mahabad River and Mahabad synoptic station were used. The results from time series simulation showed that the developed model is able to keep important statistical characteristics of historic series. In addition, it could solve the classic nearest neighbor resampling method problem in order to produce values not seen in the historical record. Also, this model showed that it could simulate the multivariate hydrologic series. | ||
| کلیدواژه‌ها [English] | ||
| Mahabad River, multivariate, nearest neighbor resampling, non-parametric model, time series | ||
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آمار تعداد مشاهده مقاله: 2,058 تعداد دریافت فایل اصل مقاله: 1,513 |
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