REAL-TIME PREDICTION OF SUSPENDED SEDIMENT CONCENTRATION USING MACHINE LEARNING: CASE STUDY OF MIWA RESERVOIR, TENRYU RIVER IN JAPAN

Abstract

Reservoir sedimentation is a critical issue that impacts dam operations by reducing storage capacity and increasing management costs. This study evaluated the effectiveness of memetic programming (MP) in predicting the suspended sediment concentration (SSC) in the Miwa Reservoir, Japan. The Miwa Dam faces challenges due to its high sediment yield and rapid discharge, necessitating accurate SSC predictions for efficient sediment management and dam operation. Hourly SSC and inflow discharge data for two periods were collected ((A) June 29-August 7, 2020, and (B) June 1-July 5, 2023). Three input scenarios were examined to predict SSC. The scenarios incorporating current and previous values of the inflow rate and SSC exhibited the highest accuracy, with correlation coefficients (R) ranging from 0.88 to 0.99 and normalized Nash-Sutcliffe coefficient (NNSC) ranging from 0.77 to 0.98. The MP model successfully captured SSC dynamics during flood events, demonstrating its potential as a valuable tool for reservoir management. Accurate real-time predictions facilitate better operational decisions, reduce sediment-related issues, and optimize reservoir functionality. This research highlights the potential of advanced machine learning techniques in sediment management, offering significant insights for reservoir sedimentation management. Future work should explore integrating additional influencing parameters, applying this approach to other reservoirs with similar challenges, and exploring the future prediction of SSC shortly for several hours in advance.