ScholarWorks@경상국립대학교

초록

Effective sediment monitoring is crucial for managing dynamic river environments where suspended sediment transport varies over time. However, manual sampling and turbidity sensor-based methods provide limited spatial coverage and can be labor-intensive. Remote sensing offers non-contact spatial measurements but generally has low temporal resolution. To overcome these challenges, we propose closed-circuit television-hyperspectral imaging for suspended sediment transport (CCTV-HISST). This framework consists of a hyperspectral CCTV system integrated with a machine learning framework and enables continuous, high-frequency monitoring of suspended sediment concentration (SSC) during the daytime, at sunset, and overnight. Combining hyperspectral imaging with low-light adaptability, the system can detect subtle spectral variations in sediments under natural and artificial lighting. We conducted 15 experiments using three sediment types (high-visibility silt, low-visibility sand, and their mixture) under controlled shallow-water conditions in an outdoor flume. Experiments were categorized by light source: sunlight for daytime, combined sunlight and halogen lighting at sunset, and halogen lighting at night. This proof-of-concept study suggests that the proposed machine learning framework, light classification and adaptive regression, achieved 99% accuracy in light classification and strong agreement with field SSC measurements, even in untrained cases. Validation using field spectrometry and laser diffraction sensor data further confirmed the reliability of the proposed system. This study highlights the potential of CCTV-HISST as a scalable, noncontact alternative for real-time monitoring by adaptively detecting suspended sediments and quantifying their concentration across a range of light conditions. Future studies can extend its applicability to natural rivers by addressing limitations related to water depth and SSC variability.

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