Research on AI Calculation Model for Average Flow Velocity of Hydrological Station Cross-section Based on ADCP
The aim of this study is to explore how to use navigation based ADCP flow velocity data and artificial intelligence technology to improve the accuracy of calculating the average flow velocity of the cross-section at Lingshui hydropower station and solve complex flow monitoring problems.By comparing the indicator flow rate method and AI based computational models,their performance and applicability under different water depth conditions were evaluated.The results indicate that in most cases,AI algorithms exhibit higher computational qualification rates and lower uncertainty,especially during high and medium water periods.However,there are still issues with data quality during low water period flow measurement,and further improvement and expansion of sample size are needed.This study provides important basis for providing more accurate and real-time hydrological information to support water resource management,flood prevention and disaster reduction,and ecological protection.
navigation ADCP data collectionaverage flow velocity of hydrological station cross-sectionAI computing model
国家科技期刊平台
NSTL
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