کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5771147 1629902 2017 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Research papersComparison of random forests and support vector machine for real-time radar-derived rainfall forecasting
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
پیش نمایش صفحه اول مقاله
Research papersComparison of random forests and support vector machine for real-time radar-derived rainfall forecasting
چکیده انگلیسی


- Real-time radar rainfall forecasting models based on RF and SVM are proposed.
- Radar rainfalls for 1- to 3-h ahead are forecasted by the proposed models.
- Single-mode and multiple-mode models based on RF and SVM are compared.
- Single-mode model gives better forecasts than multiple-mode model.
- SVM-based single-mode model performs better than RF-based single-mode model.

This study aims to compare two machine learning techniques, random forests (RF) and support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time radar-derived rainfall forecasting models use the present grid-based radar-derived rainfall as the output variable and use antecedent grid-based radar-derived rainfall, grid position (longitude and latitude) and elevation as the input variables to forecast 1- to 3-h ahead rainfalls for all grids in a catchment. Grid-based radar-derived rainfalls of six typhoon events during 2012-2015 in three reservoir catchments of Taiwan are collected for model training and verifying. Two kinds of forecasting models are constructed and compared, which are single-mode forecasting model (SMFM) and multiple-mode forecasting model (MMFM) based on RF and SVM. The SMFM uses the same model for 1- to 3-h ahead rainfall forecasting; the MMFM uses three different models for 1- to 3-h ahead forecasting. According to forecasting performances, it reveals that the SMFMs give better performances than MMFMs and both SVM-based and RF-based SMFMs show satisfactory performances for 1-h ahead forecasting. However, for 2- and 3-h ahead forecasting, it is found that the RF-based SMFM underestimates the observed radar-derived rainfalls in most cases and the SVM-based SMFM can give better performances than RF-based SMFM.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 552, September 2017, Pages 92-104
نویسندگان
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