| 大会名称 |
|---|
| 2017年 情報科学技術フォーラム(FIT) |
| 大会コ－ド |
| F |
| 開催年 |
| 2017 |
| 発行日 |
| 2017-09-05 |
| セッション番号 |
| 6G |
| セッション名 |
| 画像処理・パターン認識 |
| 講演日 |
| 2017/09/14 |
| 講演場所(会議室等) |
| 2号館4階 243号講義室 |
| 講演番号 |
| H-029 |
| タイトル |
| Water Level Prediction for Disaster Management Using Machine Learning Models |
| 著者名 |
| Tin Nilar Lin, Hiroshi Watanabe, |
| キーワード |
| water level prediction, time series analysis, KNN, SVR, linear regression |
| 抄録 |
| Prediction of a flood is one of the challenges for disaster management around the world. In this paper, we have studied and compared some useful machine learning models such as KNN, SVR and Linear Regression for getting better water level prediction. The proposed approach is applied to Ayeyarwady River in Myanmar. The future water level is predicted based on the time series data of past water levels. By the experiment, KNN (K-Nearest Neighbour) model has shown the least mean absolute error and the error rate is just 0.17%. The predicted output of the proposed model agrees in the actual water level. |
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