학술논문

Dam Inflow Prediction by Introducing Ensemble Rainfall Prediction into Machine Learning Methods / アンサンブル予測雨量を機械学習法に導入したダム流入量予測の研究
Document Type
Journal Article
Source
Artificial Intelligence and Data Science / AI・データサイエンス論文集. 2023, 4(3):976
Subject
dam inflow prediction
elastic net
ensemble rainfall prediction
sparse modeling method
storage function method
Language
Japanese
ISSN
2435-9262
Abstract
This study proposes a dam inflow forecasting method that uses ensemble prediction for precipitation to reflect prediction uncertainty. The need to improve the accuracy of dam inflow prediction for effective dam operations has increased owing to large floods that have been occurring frequently across Japan in recent years. This study focuses on a dam in Hokkaido, Japan, which has been prone to floods in recent years. Elastic Net, which is a sparse modeling method, was used to predict inflows. Meso-scale Ensemble Prediction System (MEPS), the ensemble prediction for precipitation, was introduced as an input to the model. This was compared against predictions using the storage prediction method to evaluate accuracy. The results suggest that introducing MEPS into Elastic Net can provide accurate forecasts with safe results from a flood control perspective.