PREDICTION LEARNING REALIZING THE IMPROVEMENT OF DAM IFNLOW PREDICTION PRECISION

Abstract

We have optimized the parameters of dam inflow prediction models based on certain observation data and have thrown them into real sites until now. On the other hand, we have generally thought the input data into models as uncertain predictive rain given by Japan Meteorological Agency. There are differences in data character between observation and predictive data, so we cannot expect the improvement of dam inflow prediction precision until the advance of predictive rain precision. Therefore, we offer to construct and learn models including predictive data to minimize data differences between building and operating models. In this thesis, we call this process Prediction Learning. We indicate that Prediction Learning under uncertain circumstances of operating conditions enables dam inflow prediction in operation to predict in high precision. And we show that civil engineering interpretation adding to predictive data allows the AI model to be easy, and dam inflow prediction precision one to six hours ahead is not decreasing.