An experimental field survey was conducted at the Gentaro sabo dam to evaluate the basic characteristics of a geophone placed on the spillway of the Hira River. A geophone is a device used to measure sediment discharge by picking up sounds of sediment particles that hit steel pipes located in a riverbed. This study analyzed kinetic energy and sediment discharge in relation to acoustic energy, i.e., voltage data recorded and sensitivity of detection. Results of this study showed that 1) data exceeding one particle's kinetic energy of 30 × 10 -9 kg m 2 /sec 2 ( a 2.0-mm particle or larger) should be selected when acoustic energy is analyzed; 2) a high correlation between acoustic energy and sediment discharge shows that sediment discharge can be analyzed from acoustic energy; and 3) the sensitivity of detection can be analyzed when the size of particles hitting the geophone and water depth are known.

Droughts and abnormal heavy rains have frequently occurred in Japan due to the effects of climate change in recent years, and flexible operations that maximize the functions of dams are required. Especially in cold snowy regions, snowmelt water is stored in a dam to cover water demand from early spring to early summer, but during the snowmelt season, a sudden rise in temperature and heavy rain could cause large-scale floods. Therefore, the highly accurate prediction of dam inflow during the snowmelt season is extremely important from the viewpoint of effective use of water resources and prevention of snowmelt floods. On the other hand, in recent years, research utilizing Artificial Intelligence (AI) has also been promoted in the hydrological field. This study clarified the problems of the conventional physical model (rainfall runoff model) and the prediction model by AI for the inflow of the dam during the snowmelt season in order to support efficient dam management. Then, by constructing a semi-physical model that complements the problems of the physical model and the AI model, we developed a more accurate model for predicting the inflow of snowmelt water during the snowmelt season compared to the single model.