کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8895265 | 1629899 | 2017 | 40 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events
ترجمه فارسی عنوان
یک مدل برنامه ریزی ژنتیکی باینری برای شناسایی ارتباطات بین سطح جهانی سطح دریا و حوادث بارش باران حداکثر ماهانه
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کلمات کلیدی
حداکثر بارندگی ماهانه، دمای سطح دریا، برنامه نویسی ژنتیک، طبقه بندی باینری، پیش بینی،
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
فرآیندهای سطح زمین
چکیده انگلیسی
The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 555, December 2017, Pages 397-406
Journal: Journal of Hydrology - Volume 555, December 2017, Pages 397-406
نویسندگان
Ali Danandeh Mehr, Vahid Nourani, Bahrudin Hrnjica, Amir Molajou,