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To provide a water level prediction program for improving the accuracy of a water level prediction model.SOLUTION: In an information processing device 10, a storage part 11 stores training data 13 obtained by associating a measurement value of a water level with measurement values of rainfall amounts at a plurality of times, and attenuation function information 14 obtained by associating a different value of an attenuation parameter 15a with a plurality of attenuation functions. A processing part 12 calculates a value of the attenuation parameter 15a, and a value of a model parameter 15b to be used by a water level prediction model 16 for converting the rainfall amounts at the plurality of times into an effective rainfall amount and converting the effective rainfall amount into a water level according to a specific attenuation pattern, by using the training data 13. The processing part 12 determines the water level prediction model 16 based on an attenuation function corresponding to the calculated value of the attenuation parameter 15a, and on the calculated value of the model parameter 15b. The processing part 12 predicts a water level from the determined water level prediction model 16 and a measurement value of the waterfall amounts at the plurality of times.SELECTED DRAWING: Figure 1
【課題】水位予測モデルの精度を向上させる水位予測プログラムを提供する。【解決手段】情報処理装置10において、記憶部11は、水位の計測値と複数の時刻の雨量の計測値とを対応付けた訓練データ13と、減衰パラメータ15aの異なる値と複数の減衰関数とを対応付けた減衰関数情報14とを記憶する。処理部12は、訓練データ13を用いて、減衰パラメータ15aの値と、特定の減衰パターンに応じて複数の時刻の雨量を実効雨量に変換し実効雨量を水位に変換する水位予測モデル16に用いられるモデルパラメータ15bの値とを算出する。処理部12は、算出した減衰パラメータ15aの値に対応する減衰関数と、算出したモデルパラメータ15bの値とに基づいて、水位予測モデル16を決定する。処理部12は、決定した水位予測モデル16と複数の時刻の雨量の計測値とから水位を予測する。【選択図】図1
To provide a water level prediction program for improving the accuracy of a water level prediction model.SOLUTION: In an information processing device 10, a storage part 11 stores training data 13 obtained by associating a measurement value of a water level with measurement values of rainfall amounts at a plurality of times, and attenuation function information 14 obtained by associating a different value of an attenuation parameter 15a with a plurality of attenuation functions. A processing part 12 calculates a value of the attenuation parameter 15a, and a value of a model parameter 15b to be used by a water level prediction model 16 for converting the rainfall amounts at the plurality of times into an effective rainfall amount and converting the effective rainfall amount into a water level according to a specific attenuation pattern, by using the training data 13. The processing part 12 determines the water level prediction model 16 based on an attenuation function corresponding to the calculated value of the attenuation parameter 15a, and on the calculated value of the model parameter 15b. The processing part 12 predicts a water level from the determined water level prediction model 16 and a measurement value of the waterfall amounts at the plurality of times.SELECTED DRAWING: Figure 1
【課題】水位予測モデルの精度を向上させる水位予測プログラムを提供する。【解決手段】情報処理装置10において、記憶部11は、水位の計測値と複数の時刻の雨量の計測値とを対応付けた訓練データ13と、減衰パラメータ15aの異なる値と複数の減衰関数とを対応付けた減衰関数情報14とを記憶する。処理部12は、訓練データ13を用いて、減衰パラメータ15aの値と、特定の減衰パターンに応じて複数の時刻の雨量を実効雨量に変換し実効雨量を水位に変換する水位予測モデル16に用いられるモデルパラメータ15bの値とを算出する。処理部12は、算出した減衰パラメータ15aの値に対応する減衰関数と、算出したモデルパラメータ15bの値とに基づいて、水位予測モデル16を決定する。処理部12は、決定した水位予測モデル16と複数の時刻の雨量の計測値とから水位を予測する。【選択図】図1
WATER LEVEL PREDICTION PROGRAM, WATER LEVEL PREDICTION METHOD, AND INFORMATION PROCESSING DEVICE
水位予測プログラム、水位予測方法および情報処理装置
SO MISAKO (author)
2021-09-16
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
/
E02B
HYDRAULIC ENGINEERING
,
Wasserbau
/
G01W
METEOROLOGY
,
Meteorologie
/
G06N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
,
Rechnersysteme, basierend auf spezifischen Rechenmodellen
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