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A platform for research: civil engineering, architecture and urbanism
METHOD FOR PREDICTING WATER LEVEL IN SEWAGE PIPE SEWER
To achieve accurate prediction of the water level in a sewage pipe sewer, the prediction in which changes in both directions of a spatial direction and a time direction are appropriately converted into feature quantities while reducing time and cost for constructing a prediction model.SOLUTION: In a device for predicting the water level in a sewage pipe sewer 10, a predictive input data generation unit 15 converts a data group of actual state values and predicted values of mesh precipitation at different times in an area including a prediction target point into a format applicable to convolution processing, and thereby generates input data for prediction submitted to a convolutional neural network of a water level prediction value calculation unit 16 that predicts the water level at the prediction target point in the sewage pipe sewer. In predicting the water level in the sewage pipe sewer in the convolutional neural network, convolution processing and MaxPooling processing are executed on the input data for prediction. Subsequently, the water level at the prediction target point in the sewage pipe sewer is predicted from data of a result of MaxPooling processing and data of the water level in the sewage pipe sewer at a measuring point near to the prediction target point.SELECTED DRAWING: Figure 1
【課題】下水道管渠内水位の予測にあたり、予測モデル構築の時間とコストを抑えながら、空間方向および時間方向、双方向の変化を適切に特徴量化した高精度な予測を図る。【解決手段】下水道管渠内水位予測装置１０において、予測入力データ生成部１５は予測対象地点を含む地域の時刻の異なるメッシュ降水量の実況値及び予測値のデータ群を畳み込み処理の適用可能な形式に変換することにより当該予測対象地点の下水道管渠内水位を予測する水位予測値算出部１６の畳み込みニューラルネットワークに供される予測用入力データを作成する。前記畳み込みニューラルネットワークにおいて前記下水道管渠内水位を予測にあたり前記予測用入力データに対して畳み込み処理及びMaxPooling処理が施される。次いで、MaxPooling処理結果のデータと予測対象地点の近傍測定点の下水道管渠内水位データとから当該予測対象地点の下水道管渠内水位を予測する。【選択図】図１
METHOD FOR PREDICTING WATER LEVEL IN SEWAGE PIPE SEWER
To achieve accurate prediction of the water level in a sewage pipe sewer, the prediction in which changes in both directions of a spatial direction and a time direction are appropriately converted into feature quantities while reducing time and cost for constructing a prediction model.SOLUTION: In a device for predicting the water level in a sewage pipe sewer 10, a predictive input data generation unit 15 converts a data group of actual state values and predicted values of mesh precipitation at different times in an area including a prediction target point into a format applicable to convolution processing, and thereby generates input data for prediction submitted to a convolutional neural network of a water level prediction value calculation unit 16 that predicts the water level at the prediction target point in the sewage pipe sewer. In predicting the water level in the sewage pipe sewer in the convolutional neural network, convolution processing and MaxPooling processing are executed on the input data for prediction. Subsequently, the water level at the prediction target point in the sewage pipe sewer is predicted from data of a result of MaxPooling processing and data of the water level in the sewage pipe sewer at a measuring point near to the prediction target point.SELECTED DRAWING: Figure 1
【課題】下水道管渠内水位の予測にあたり、予測モデル構築の時間とコストを抑えながら、空間方向および時間方向、双方向の変化を適切に特徴量化した高精度な予測を図る。【解決手段】下水道管渠内水位予測装置１０において、予測入力データ生成部１５は予測対象地点を含む地域の時刻の異なるメッシュ降水量の実況値及び予測値のデータ群を畳み込み処理の適用可能な形式に変換することにより当該予測対象地点の下水道管渠内水位を予測する水位予測値算出部１６の畳み込みニューラルネットワークに供される予測用入力データを作成する。前記畳み込みニューラルネットワークにおいて前記下水道管渠内水位を予測にあたり前記予測用入力データに対して畳み込み処理及びMaxPooling処理が施される。次いで、MaxPooling処理結果のデータと予測対象地点の近傍測定点の下水道管渠内水位データとから当該予測対象地点の下水道管渠内水位を予測する。【選択図】図１
METHOD FOR PREDICTING WATER LEVEL IN SEWAGE PIPE SEWER
下水道管渠内水位予測方法
KIMURA YUKI (author) / FUKAI HIRONAGA (author)
2021-11-25
Patent
Electronic Resource
Japanese
IPC:
G06Q
Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke
,
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES
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E03F
SEWERS
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Abwasserkanäle
/
G01W
METEOROLOGY
,
Meteorologie
/
G06N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
,
Rechnersysteme, basierend auf spezifischen Rechenmodellen
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