Monthly Precipitation Forecasting with a Neuro-Fuzzy Model

Changsam Jeong, Ju Young Shin, Taesoon Kim, Jun Haneg Heo

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4467-4483
Number of pages17
JournalWater Resources Management
Issue number15
Publication statusPublished - 2012 Oct

Bibliographical note

Funding Information:
Acknowledgement This research was supported by a grant (11-TI-C06) from Construction Technology Innovation Program funded by Ministry of Land, Transport and Maritime Affairs of Korean government.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Water Science and Technology


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