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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2014.1
- 수록면
- 123 - 133 (11page)
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초록· 키워드
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It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.
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참고문헌 (23)
참고문헌 신청
Box, G.E.P / 1976 / Time Series Analysis-Forecasting and Control / Holden-Day :575
Coulibaly, P / 2001 / Artificial neural network modeling of water table depth fluctuations / Water Resour. Res 37(4):885~896
French, M.N / 1992 / Rainfall forecasting in space and time using a neural network / J. Hydrol 137(1-4):1~31
Herrera, L. J / 2007 / Recursive prediction for long term time series forecasting using advanced models / Neurocomputing 70(16-18):2870~2880
Hipel, K. W / 1977 / Advances in Box-Jenkins modeling: 1. Model construction / Water Resour. Res 13(3):567~575
Coulibaly, P / 2001 / Artificial neural network modeling of water table depth fluctuations / Water Resour. Res 37(4):885~896
French, M.N / 1992 / Rainfall forecasting in space and time using a neural network / J. Hydrol 137(1-4):1~31
Herrera, L. J / 2007 / Recursive prediction for long term time series forecasting using advanced models / Neurocomputing 70(16-18):2870~2880
Hipel, K. W / 1977 / Advances in Box-Jenkins modeling: 1. Model construction / Water Resour. Res 13(3):567~575
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