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  1. 学術論文等

水位推定誤差の確率分布に基づく河川水位観測データのリアルタイム異常検知

https://uec.repo.nii.ac.jp/records/9960
https://uec.repo.nii.ac.jp/records/9960
66e0be17-1543-46d6-a25a-778007aff6d3
名前 / ファイル ライセンス アクション
jscejhe.75.2_I_193.pdf jscejhe.75.2_I_193 (1.2 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2021-04-14
タイトル
言語 ja
タイトル 水位推定誤差の確率分布に基づく河川水位観測データのリアルタイム異常検知
タイトル
言語 en
タイトル REAL-TIME ANORMALY DETECTION OF RIVER WATER LEVEL OBSERVATION BASED ON PROBABILITY DISTRIBUTION OF WATER LEVEL ESTIMATION ERROR
言語
言語 jpn
キーワード
言語 en
主題Scheme Other
主題 anomaly detection
キーワード
言語 en
主題Scheme Other
主題 river water level
キーワード
言語 en
主題Scheme Other
主題 machine learning
キーワード
言語 en
主題Scheme Other
主題 deep learning
キーワード
言語 en
主題Scheme Other
主題 flood prediction
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者 一言, 正之

× 一言, 正之

WEKO 26658

ja 一言, 正之

ja-Kana ヒトコト, マサユキ

Search repository
川越, 典子

× 川越, 典子

WEKO 26659

ja 川越, 典子

ja-Kana カワゴエ, ノリコ

Search repository
橋田, 創

× 橋田, 創

WEKO 26660

ja 橋田, 創

ja-Kana ハシダ, ハジメ

Search repository
清, 雄一

× 清, 雄一

WEKO 26661

ja 清, 雄一

ja-Kana セイ, ユウイチ

Search repository
房前, 和朋

× 房前, 和朋

WEKO 26662

ja 房前, 和朋

ja-Kana フサマエ, カズトモ

Search repository
HITOKOTO, Masayuki

× HITOKOTO, Masayuki

WEKO 26668

en HITOKOTO, Masayuki

Search repository
KAWAGOE, Noriko

× KAWAGOE, Noriko

WEKO 26669

en KAWAGOE, Noriko

Search repository
HASHIDA, Hajime

× HASHIDA, Hajime

WEKO 26670

en HASHIDA, Hajime

Search repository
SEI, Yuichi

× SEI, Yuichi

WEKO 26671

en SEI, Yuichi

Search repository
FUSAMAE, Kazutomo

× FUSAMAE, Kazutomo

WEKO 26672

en FUSAMAE, Kazutomo

Search repository
抄録
内容記述タイプ Abstract
内容記述 水位計による河川水位のオリジナル観測データには,各種の異常値が含まれる.観測水位の異常値は,防災対応の判断や洪水予測システムに致命的なエラーを引き起こす可能性があるが,リアルタイムでの異常検知は十分に行われていない.本研究では,10分毎に観測所から配信される河川水位データを対象として,リアルタイムに異常値を検出する技術を開発した.機械学習による水位推定手法の技術を用いて,周辺の水位・雨量状況から対象とする観測地点の現時刻の水位を推定し,実観測データとの乖離度合いから異常度を算出した.さらにルールベースによる異常検知と組み合わせ,検知性能の向上を図った.九州管内の実データを用いて提案手法の精度検証を行い,既存手法と比較して高い検知性能を確認した.
抄録
内容記述タイプ Abstract
内容記述 Real-time observation data of the river water level includes various anomalies. Such anomalies may cause fatal errors in judgements on disaster prevention activity and flood forecasting systems, but reat-time anomaly detection has not been sufficiently implemented. In this study, we developed the model to detect anomalies in real-time for river water level data sent from observation station every 10 minutes. By using machine learning, the water level at the current time of the objective observation station was estimated from the neighboring water level and rainfall. Then the anomaly score was calculated from the degree of deviation between the estimated water level and actual obserbation. Furthermore,the model was combined with the rule-based anomaly detection model. The proposed method was verified using actual obsevation data, and better performance was cnfirmed compared to the existing method.
書誌情報 ja : 土木学会論文集B1(水工学)
en : Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering)

巻 75, 号 2, p. I193-I198, 発行日 2019
出版者
出版者 土木学会
ISSN
収録物識別子タイプ ISSN
収録物識別子 2185467X
DOI
関連タイプ isIdenticalTo
識別子タイプ DOI
関連識別子 10.2208/jscejhe.75.2_I_193
権利
権利情報 © 2019 公益社団法人 土木学会
関連サイト
識別子タイプ DOI
関連識別子 https://doi.org/10.2208/jscejhe.75.2_I_193
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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