Monthly Precipitation Forecasting with a Neuro-Fuzzy Model

被引：0
作者：
Changsam Jeong
Ju-Young Shin
Taesoon Kim
Jun-Haneg Heo
机构：
[1] Induk University,Department of Civil and Environmental Eng
[2] Yonsei University,School of Civil and Environmental Engineering
[3] Korea Hydro & Nuclear Power Co. Hydroelectric Powers,undefined
来源：
Water Resources Management | 2012年 / 26卷
关键词：
Neuro-fuzzy; Input data selection; ANFIS; Long-term forecast;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Quantitative and qualitative monthly precipitation forecasts are produced with ANFIS. To select the proper input variable set from 30 variables, including climatological and hydrological monthly recording data, the forward selection method, which is a wrapper method for feature selection, is applied. The error analysis of the results from training and checking the data sets suggests that 3 variables can be used as a suitable number of inputs for ANFIS, and the best five 3-input-variable sets were selected. The quantitative monthly precipitation forecasts were computed using each 3-input-variable set, and the ensemble averaging method over the five forecasts was used for calculations to reduce the uncertainties in the forecasts and to remove the negative rainfall forecasts. A qualitative forecast that is computed with the quantitative forecast also produced three types of categories that describe the next month’s precipitation condition and was compared with data from the weather agency of Korea.
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页码：4467 / 4483
页数：16
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