Name
Title | ||
|---|---|---|
Forecasting of Reservoir Inflow by the Combination of Deep Learning and Conventional Machine Learning |
Abstract | ||
|---|---|---|
Recently, forecasting of inflow to a reservoir by employing machine learning techniques is getting attention for maximization of generation of hydroelectricity and prevention of disaster caused by flooding. In this context, forecasting of peak inflow caused by heavy rain or melting of snow is of utmost importance to properly utilize water for hydroelectricity, limit the damage to the reservoir/dam... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDMW53433.2021.00074 | 2021 International Conference on Data Mining Workshops (ICDMW) |
Keywords | DocType | ISSN |
Deep learning,Learning systems,Dams,Weather forecasting,Hydroelectric power generation,Predictive models,Reservoirs | Conference | 2375-9232 |
ISBN | Citations | PageRank |
978-1-6654-2427-1 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Topon Paul | 1 | 0 | 0.34 |
| Sreeharsha Raghavendra | 2 | 0 | 0.34 |
| Ken Ueno | 3 | 0 | 0.34 |
| Fang Ni | 4 | 0 | 0.34 |
| Hiromasa Shin | 5 | 0 | 0.34 |
| Kaneharu Nishino | 6 | 0 | 0.34 |
| Ryusei Shingaki | 7 | 0 | 0.34 |