SEQUENTIAL PREDICTION OF DALLY GROUNDWATER LEVELS BY A NEURAL NETWORK MODEL BASED ON WEATHER FORECASTS
This paper investigates the implementation of an Artificial Neural Network (ANN)model for sequential prediction of daily groundwater levels based on precipitation forecasts. The basic principle of the ANN-based procedure consists of relating previous daily groundwater levels and daily precipitation forecasts in order to predict daily groundwater levels up to seven days ahead. The daily precipitation values up to one week ahead are assumed to be deterministic since meteorological short-range forecasts are generally available. The methodology is applied to the groundwater system of Matsuyama City, Japan. Insufficiency of water is a periodical problem in this city and thus accurate predictions of groundwater levels are very important to improve the water resources management in the region. The excellent accuracy obtained by the ANN model indicates that it is very efficient for the multi-step-ahead prediction of daily groundwater levels. As conclusion, this methodology may provide trustworthy data for the application of models to the sustainable management of Matsuyamas groundwater system.
groundwater level artificial neural networks sequential prediction precipitation forecasts
C.A.S.Farias K.Suzuki A.Kadota
Dept.of Civil and Environmental Engineering, Ehime University, Matsnyama, Ehime, Japan Dept.of Civil and Environmental Engineering, Ehime University, Matsuyama, Ehime, Japan Dept.of Civil and Environmental Eng., Ehime University, Matsuyama, Ehime, Japan
国际会议
第16届亚太地区国际水利学大会暨第3届水工水力学国际研讨会(16th IAHR-APD Congress and 3rd Symoposium of IAHR-ISHS)
南京
英文
225-230
2008-10-20（万方平台首次上网日期，不代表论文的发表时间）