深層学習を用いた河川水位予測手法の開発

The real-time river stage prediction model is developed, using the artificial neural network model which is trained by the deep learning method. The model is composed of 4 layer feed-forward network. As a network training method, stochastic gradient descent method based on the back propagation method was applied. As a pre-training method, the denoising autoencoder was applied. The developed model is applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. Input of the model is hourly change of water level and hourly rainfall, output data is water level of HIWATASHI. To clarify the suitable configuration of the model, case study was done. The prediction result is compared with the other prediction models, consequently the developed model showed the best performance

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

　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．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