| Project/Area Number |
17K01339
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Natural disaster / Disaster prevention science
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| Research Institution | Keio University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | センサ / IoT / 機械学習 / 可視化 / クラウド / 時系列データ / 環境計測 / 予測 / 警告 / 社会実装 / 時系列 / Internet of things / 自然災害予測 |
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| Outline of Final Research Achievements |
We researched and developed a system that measures and collects environmental information using sensors and IoT technology, and provides time-series data prediction and warning on a cloud server. In the first year, we mainly developed hardware and conducted demonstration experiments and visualization. In the third year, we conducted machine learning using Long Short Term Memory (LSTM) for prediction from time-series environmental measurement data. In the second year, we conducted machine learning using Long Short Term Memory (LSTM) for forecasting from time-series environmental measurement data. As for social implementation, we developed solar-powered IoT PM2.5, NO2, temperature and humidity meters and installed them in Uganda, East Africa, to improve the accuracy of weather forecasting.
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| Academic Significance and Societal Importance of the Research Achievements |
研究期間を通じて申請書記載の３つの技術課題を達成することで「途上国向けIoT環境モニタリングポスト」を実現して途上国が導入・長期運用可能な装置を実現する事ができた。高耐久自立電源が実現できた事から、電源がない地方や山林・荒野における環境モニタリングが増加させるすることができ大気環境の定量化が可能となる。このデータと機械学習予測を組み合わせることで大気環境の把握が可能となり適切な環境改善対策の立案や防災に生かすことが可能となる。さらに、IoT技術によりクラウドを介在させ可視化しつつ長時間の環境データを蓄積して環境研究に役立てる事や市民が環境情報にアクセスすることで健康被害を防ぐことが可能となる。
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