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[Objective] To analyze the scale-dependent relationships between daily sediment yield and key environmental factors and to explore effective prediction method to cope with the prediction challenges caused by the complexity of sediment transport mechanisms and the nonlinearity of hydrological processes. [Methods] Employing the Composite Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) approach, we analyzed the multi-temporal scale variability characteristics between daily sediment yield and five potential influencing variables (flow rate, precipitation, average temperature, potential evapotranspiration, and NDVI) in the controled regions of four hydrological stations in the Fu River basin of Southwest China from 2013 to 2018. Interactions between daily sediment yield and related variables at different temporal scales were identified, upon which predictions of daily sediment yield were predicted. [Results] The CEEMDAN method successfully decomposed the daily sediment yield and its potential influencing variables into 10-11 intrinsic mode functions (IMFs) and residual item, revealing the significant changes of the daily sediment yield at different temporal scales ranging from 3 to 730 days. After decomposition, daily sediment yield showed significantly enhanced correlation with howrate and precipitation across all temporal scales, whereas temperature, potential evapotranspiration, and NDVI were primarily significantly correlated with long-term scale sediment yields. By using stepwise multiple linear regression to predict the daily sediment yield, the prediction performance of the model is significant, and the R2 values exceeds 0.55 at all stations. [Conclusion] The CEEMDAN method effectively revealed the scale dependency between sediment yield and its influencing variables, offering a new perspective for understanding the dynamics of river sediment transport.

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Aiming at the problem of single data recognition in pantograph-catenary arc recognition method, a pantograph-catenary arc recognition method based on improved multi-scale convolutional network ( GInception ) combined with LightGBM is proposed. The original signal is denoised and reconstructed by using the combination of complete ensemble empirical mode decomposition ( CEEMDAN ) and wavelet soft threshold. The reconstructed signal is broadcasted and transformed into a multi-dimensional signal, which is input into the GInception network for feature extraction, and then the features extracted by the GInception network are imported into the lightweight gradient hoist for recognition. The results show that the recognition accuracy of pantograph-catenary arc reaches 96.3 % under five different working conditions. The research conclusions can provide reference for the identification of pantograph-catenary arc in trams. [ABSTRACT FROM AUTHOR]

In order to study the characteristics and evolution of water and sediment in Inner Mongolia reach of the Yellow River and analyze the relationship between water and sediment, the R/S analysis method, cumulative anomaly method, Mann-Kendall（M-K） mutation test method and wavelet period test method were used to analyze the runoff/sediment data of Toudaoguai Hydrological Station in Inner Mongolia reach of the Yellow River from 1956 to 2018, with the characteristics of water and sediment situation evolution over the years concluded and the key factors affecting the change process of water and sediment movement discussed. According to the analysis results, both the runoff and sediment transport from 1956 to 2018 at Toudaoguai Station show a significant downward trend and the annual runoff and sediment transport both have abrupt changes around 1986. The Hurst index values of runoff and sediment transport at Toudaoguai Station are 0.609 and 0.658 respectively, which are consistent for a long period of time with previous trends. The evolution of runoff and sediment volume series has a multi-scale cycle. As the cycle is decomposed, the mutual information value of runoff and sediment volume shows an increasing trend. The annual runoff in the change period shows a stepwise downward trend. Compared with the base period（1956-1986）, the mean runoff in the change period（1987-2018） is reduced by 33.37% and in the change period of sediment discharge is reduced by 68.91%. Water-sediment evolution in the Inner Mongolia reach is more suitable for medium and long cycle studies. The evolution of runoff and sediment is affected by multiple factors, such as precipitation, water conservancy project, artificial water extraction project and water and soil conservation engineering. Among them, water conservancy project, artificial water extraction project and water and soil conservation engineering play an important role in the variation of river water and sediment. [ABSTRACT FROM AUTHOR]