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  1. 防災科研関係論文

Accurate and early detection of Localized Heavy Rain by integrating multivendor sensors in various installation environments

https://nied-repo.bosai.go.jp/records/6609
https://nied-repo.bosai.go.jp/records/6609
3108e0b5-be16-4f5b-b0b1-7c0fcb139c40
Item type researchmap(1)
公開日 2024-05-13
タイトル
言語 en
タイトル Accurate and early detection of Localized Heavy Rain by integrating multivendor sensors in various installation environments
著者 K. Hiroi

× K. Hiroi

en K. Hiroi

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Yoshihito Seto

× Yoshihito Seto

en Yoshihito Seto

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Futoshi Matsumoto

× Futoshi Matsumoto

en Futoshi Matsumoto

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Yuzo Taenaka

× Yuzo Taenaka

en Yuzo Taenaka

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Hideya Ochiai

× Hideya Ochiai

en Hideya Ochiai

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Haruo Ando

× Haruo Ando

en Haruo Ando

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Hitoshi Yokoyama

× Hitoshi Yokoyama

en Hitoshi Yokoyama

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Masaya Nakayama

× Masaya Nakayama

en Masaya Nakayama

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Hideki Sunahara

× Hideki Sunahara

en Hideki Sunahara

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抄録
内容記述タイプ Other
内容記述 In this study, we focus on the accurate and early prediction of Localized Heavy Rain (LHR) using multiple sensors. Traditional sensors, such as rain gauges and radar, cannot detect LHR until cumulonimbus clouds cover the sensors. In contrast, Surface Meteorological Monitoring Networks (SMMNs) can accurately measure rainfall in the vicinity of the sensors, thereby detecting LHR earlier than traditional sensors. By evenly placing the sensors around a large city, a SMMN should be useful in predicting LHR. However, since most sensors are placed in a different installation environment, their raw sensor data may significantly differ depending on their surrounding environment (i.e., altitude and sky view factor). Therefore, we propose a calibration scheme for a SMMN that utilizes many sensors in various installation environments and implement a novel LHR prediction system that produces accurate and early LHR predictions. Our system proved to accurately predict LHR 30 minutes earlier than traditional schemes. © 2013 IEEE.
言語 en
書誌情報 en : Proceedings of IEEE Sensors

発行日 2013
DOI
関連識別子 10.1109/ICSENS.2013.6688472
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