수리지질학․10월 29일(토)
2005 대한지질학회 추계학술발표회 초록집
143Forecasting Hydrogeologic Time Series Data Using Artificial Neural Networks
Yoon, Heesung*․Jun, Seong-Chun․ Bae, Kwang-Ok․ Lee, Kang-Kun Hydrogeology Laboratory, Seoul National University, [email protected]
An effective management of the groundwater requires the forecasting temporal varia- tions of hydrogeologic variables, such as the level of groundwater table, the concentration of contaminants in groundwater and so on. These hydrogeologic time series data tend to have a nonlinear relationship between input and output time series because the subsur- face medium is highly heterogeneous. The conventional time series forecasting models, however, are based on a linear relationship between inputs and outputs. Thus they have difficulties in forecasting the hydrogeologic time series data whose relationship between inputs and outputs is nonlinear.
Recently, the application of artificial neural networks (ANN) as an approach to fore- casting water resource variables is growing. The ANN is a flexible mathematical structure patterned after a biological nerval system and is considered the standard computational tool for nonlinear problems in a variety of fields.
In this study, the applicability of an ANN time series model to forecasting the level of groundwater table is investigated. For the model development feed forward network and recurrent neural network are used. The input time series are the amount of the precip- itation or the precipitation and the level of the tide in the case of the coastal site. The opti- mal model structure and parameters are examined and the effect of lag times between in- puts and outputs on model performance is also investigated. The forecasting results show that the ANN time series model can be an effective method to forecast the hydrogeologic time series data.
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