Hydrodynamic characteristics of Wujiangdu Reservoir during the dry season-a case study of a canyon reservoir

With the development of hydropower in the karst area of Southwest China, a series of cascade canyon reservoirs have been formed through the construction of dams. Given that hydrodynamic conditions in canyon reservoirs play a pivotal role in controlling the spatiotemporal distribution of physical and chemical properties of the stored water, hydrodynamic characteristics are of great importance in understanding biogeochemical cycles in those reservoirs. To further this understanding, a field campaign was conducted in the Wujiangdu Reservoir of Guizhou Province. It was found that from the reservoir inlet to the front of the dam, velocity(v) was negativelycorrelated and had a logarithmic relationship with distance along the ship track(s) under dry-season flow conditions[v =-0.104 ln(s) + 0.4756]. Analysis showed that dryseason flow velocity had no significant correlation with water temperature, p H, or dissolved oxygen(DO). However, when velocity decreased to 0.061 m/s, water depth increased abruptly. In addition, DO displayed a sudden drop and the trend in p H changed from increasing to decreasing, while water temperature showed an opposite trend, indicating the existence of a transition zone from the river to the reservoir.

The Multisource Time Series Data Granularity Conversion Method

Granular information has emerged as a potent tool for data represen-tation and processing across various domains.However,existing time series data granulation techniques often overlook the influence of external factors.In this study,a multisource time series data granularity conversion model is proposed that achieves granularity conversion effectively while maintaining result consis-tency and stability.The model incorporates the impact of external source data using a multivariate linear regression model,and the entropy weighting method is employed to allocate weights andfinalize the granularity conversion.Through experimental analysis using Beijing’s 2022 air quality dataset,our proposed method outperforms traditional information granulation approaches,providing valuable decision-making insights for industrial system optimization and research.

Rolling Iterative Prediction for Correlated Multivariate Time Series

Correlated multivariate time series prediction is an effective tool for discovering the chang rules of temporal data,but it is challenging tofind these rules.Recently,deep learning methods have made it possible to predict high-dimensional and complex multivariate time series data.However,these methods cannot capture or predict potential mutation signals of time series,leading to a lag in data prediction trends and large errors.Moreover,it is difficult to capture dependencies of the data,especially when the data is sparse and the time intervals are large.In this paper,we proposed a prediction approach that leverages both propagation dynamics and deep learning,called Rolling Iterative Prediction(RIP).In RIP method,the Time-Delay Moving Average(TDMA)is used to carry out maximum likelihood reduction on the raw data,and the propagation dynamics model is applied to obtain the potential propagation parameters data,and dynamic properties of the correlated multivariate time series are clearly established.Long Short-Term Memory(LSTM)is applied to capture the time dependencies of data,and the medium and long-term Rolling Iterative Prediction method is established by alternately estimating parameters and predicting time series.Experiments are performed on the data of the Corona Virus Disease 2019(COVID-19)in China,France,and South Korea.Experimental results show that the real distribution of the epidemic data is well restored,the prediction accuracy is better than baseline methods.