Feasibility of using artificial neural networks to forecast groundwater levels in real time

The purpose of this study is to develop the feed-forward back-propagation neural network (FFBPNN) to estimate the groundwater level (GL) of next hour according the current GL and past precipitation depth in the hillslope. The 72-h precipitation depth and the real-time groundwater levels are used as the model output layer determination variables. The output variables, are type 1, the GL, which has been used in many researches, and type 2, the groundwater level fluctuation (GLF), which is the difference between the current-time and the next-time groundwater level. The order of the water level fluctuation is less than that of the groundwater level by about one order of magnitude (ten times). The landslide area at the downstream of Wu-She Reservoir, Nantou County, Taiwan, is adopted as a field test area. Total 328 cases of Sinlaku typhoon were used to establish the prediction model of real-time GL. Another 327 cases of Jangmi typhoon were adopted to illustrate the model application. The result of model application shows that root-mean-square error of type 2 (=0.104 m) is smaller than that of type 1 (=0.408 m). In conclusion, the forecasting method used GLF gives a much better agreement with the measured values than that of GL.