| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Outline of Final Research Achievements |
This study tested various hydrologic model such as, Kinematic Wave model, Tank model, Artificial Neural Network model (ANN), to reproduce and reanalysis the long-term hydrologic data, especially for river discharge data. Among those tested hydrological models, ANN model provides plausible results with its modeling flexibility and estimation performance. ANN model allows us to model by linking any variables that are related without physical connectivity and physical concept behind.
First application was on the long-term dam inflow estimation for Naramata Dam reservoir at the upper basin of the Tone river. Second application was on water stage estimation by utilizing the water stage information from neighboring water gauge stations. Both results provide prominent results to reproduce hydrologic data for long-term data reanalysis.
|
