| Presentation | 2019-09-14 Prediction of water level in Kinu River using recurrent neural networks Takehiko Ito, Ryo Kaneko, Tomoya Kataoka, Shiho Onomura, Yasuo Nihei, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Improving the accuracy of flood prediction in rivers is an urgent task as a countermeasure against heavy rain disasters caused by climate change. In this study, we researched about water level prediction during flood term using recurrent neural networks. We apply it to Kinu river with steep and gentle slope sections. As a result, the accuracy of one upstream location of the all the five predicted points remained a problem, but the accuracy of the other four points was good. Moreover, it was shown that making and learning simulated flood data is effective for forecasting the largest flood in the past. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | flood prediction / water level / forecasting / recurrent neural network / LSTM / kinu river |
| Paper # | AI2019-26 |
| Date of Issue | 2019-09-06 (AI) |
| Conference Information | |
| Committee | AI |
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| Conference Date | 2019/9/13(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Naoki Fukuta(Shizuoka Univ.) |
| Vice Chair | Yuichi Sei(Univ. of Electro-Comm.) / Yuko Sakurai(AIST) |
| Secretary | Yuichi Sei(Osaka Univ.) / Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Prediction of water level in Kinu River using recurrent neural networks |
| Sub Title (in English) | |
| Keyword(1) | flood prediction |
| Keyword(2) | water level |
| Keyword(3) | forecasting |
| Keyword(4) | recurrent neural network |
| Keyword(5) | LSTM |
| Keyword(6) | kinu river |
| 1st Author's Name | Takehiko Ito |
| 1st Author's Affiliation | Tokyo University of Science(TUS) |
| 2nd Author's Name | Ryo Kaneko |
| 2nd Author's Affiliation | Tokyo University of Science(TUS) |
| 3rd Author's Name | Tomoya Kataoka |
| 3rd Author's Affiliation | Tokyo University of Science(TUS) |
| 4th Author's Name | Shiho Onomura |
| 4th Author's Affiliation | Tokyo University of Science(TUS) |
| 5th Author's Name | Yasuo Nihei |
| 5th Author's Affiliation | Tokyo University of Science(TUS) |
| Date | 2019-09-14 |
| Paper # | AI2019-26 |
| Volume (vol) | vol.119 |
| Number (no) | AI-202 |
| Page | pp.pp.43-44(AI), |
| #Pages | 2 |
| Date of Issue | 2019-09-06 (AI) |