Proceedings of the Korea Water Resources Association Conference (한국수자원학회:학술대회논문집)
- 2015.05a
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- Pages.398-398
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- 2015
Application of the Artificial Neurons Networks Model uses under the condition of insufficient rainfall data for Runoff Forecasting in Thailand
- Mama, Ruetaitip (Dept. of Civil Eng., Chungnam National University) ;
- Jung, Kwansue (Dept. of Civil Eng., Chungnam National University) ;
- Kim, Minseok (Dept. of Civil Eng., Chungnam National University)
- Published : 2015.05.27
Abstract
To estimate and forecast runoff by using Aritifitial Neaural Networks model (ANNs). it has been studied in Thailand for the past 10 years. The model was developed in order to be conformed with the conditions in which the collected dataset is short and the amount of dataset is inadequate. Every year, the Northerpart of Thailand faces river overflow and flood inundation. The most important basin in this area is Yom basin. The purpose of this study is to forecast runoff at Y.14 gauge station (Si-Satchanalai district, Sukhothai province) for 3 days in advance. This station located at the upstream area of Yom River basin. Daily rainfall and daily runoff from Royal Irrigation Department and Meteorological Department during flood period 2000-2012 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (
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