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Seno, Shoji*; Kunimaru, Takanori; Nakajima, Makoto*; Toida, Masaru*; Watanabe, Kunio*; Sohail, A. R.*
JAEA-Research 2008-126, 120 Pages, 2009/12
JAEA-Research-2008-126.pdf:29.29MB
At first, to exclude the influences of working of nature such as tide and atmospheric pressure from the source data, an analysis with Bayesian model was progressed. As the result of the estimation of these influences calculated by BAYTAP-G (Bayesian Tidal Analysis Program Grouping Model), it was found that the influence of the atmospheric pressure was comparatively large and that of tide was comparatively small.
Sohail, A. R.*; Watanabe, Kunio*; Takeuchi, Shinji
Chikasui Gakkai-Shi, 48(4), p.233 - 262, 2006/11
Runoff analysis for precise prediction of discharge was carried out by artificial neural network model with real coded genetic algorithm (GAANN), back propagation artificial neural network model (BPANN) and multivariate autoregressive moving average model (MARMA). It was found that for very small catchments seasonal effect on the runoff is dominant. It was also found that estimation by ANN models was better than MARMA model for analyzing the responses to intense rainfalls in summer. The accuracy of the forecasts after several time periods in future was also investigated and found to decrease as time period is increased.
Seno, Shoji*; Toida, Masaru*; Watanabe, Kunio*; Sohail, A. R.*; Kunimaru, Takanori
no journal, ,
Horonobe Underground Research Center of Japan Atomic Energy Agency has been conducting long-term observation of the pore water pressure fluctuation as part of the research activities in the Horonobe Underground Research Project. The pore water pressures are now monitored at 70 points from 100 to 1,000m in depth of 9 boreholes. In this article, some generic algorithm (GA) method and neural network (BPANN, GAANN) methods were applied to the observed pore water pressure data. The cross-correlations for a single borehole data and for different two boreholes data, or for a pore water pressure data with any other observed data (e.g. earth tide, atmospheric pressure, groundwater level, river water level and flow rates) were investigated and used for the prediction analysis.
