Flow Time Series Research Articles

Use of singular spectral analysis to reconstruct average monthly flow time series with complex behavior

Time series analysis is a useful tool for many practical water resource management applications, such as planning and anticipating conflict around water use. These analyses are also necessary for the use, design and operation of hydrotechnical works and for the protection of aquatic ecosystems. Basically, his review focuses on classic models such as Box and Jenkins. However, restrictive assumptions about data and residuals make its use difficult. As an alternative, non-parametric approaches are interesting due to their application versatility. One of the techniques that has been widely used in the analysis of flow time series is Singular Spectral Analysis (SSA). However, its application is concentrated as a pre-processor or in hybrid approaches and, because of this, many studies focus only on the model to be improved, leaving gaps in the process understanding that underlie the SSA. Within this context, and because of this, the objective of this study is to present the SSA technique in detail, through an application to a time series of monthly average flows with complex behavior (Jucu River, located in Southeast Brazil), in order to overcome gaps associated to a rigorous understanding of the procedures that make up the SSA. The Sequential SSA technique was used to decompose signal and noise components. The reconstructed series preserved the dynamics of the observed series, suggesting a strong influence of the signal component (trend and seasonality) on its behavior. It’s expected that the technique can be used with greater fluidity by specialists and contribute to the management of water resources.
 Keywords: decomposition, reconstruction, singular spectrum analysis.

Alternate pathway for regional flood frequency analysis in data-sparse region

Accurately analyzing flood frequency is crucial for developing effective flood management strategies and designing flood protection infrastructure, but the complex and nonlinear nature of the hydrological system poses significant challenges. The use of advanced statistical techniques, hydrological modeling, and computational tools has facilitated the implementation of several regional flood frequency analysis (RFFA) methods, including machine learning (ML) approaches. However, such regional methods, being statistical and data-intensive with limited hydrologic inferences, can be challenging for regions with limited data. In this study, we evaluated an alternate approach or path for RFFA in two contrasting regions: a data-sparse region in India and a data-dense region in the USA. The alternate approach involves using deep learning (DL)-based model for streamflow prediction, followed by at-site flood frequency analysis to estimate flood quantiles. The proposed alternate approach was compared with the classical RFFA method, which utilized the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to establish the intricate relationship between different flood quantiles and predictor variables related to physio-meteorological conditions. The findings showed that the alternate approach outperformed the classical RFFA method in the data-sparse region of India, reducing the mean absolute error (MAE) and root mean squared error (RMSE) by approximately 50 %, and achieving a higher coefficient of determination (R2 = 0.85 to 0.96). In the data-dense region, the classical and alternate approaches produced comparable results. However, the alternate approach has the benefit of flexibility and offers a complete time series of daily flow at the ungauged watersheds, enabling the estimation of other flow attributes or magnitudes for any return period without needing a separate classical RFFA model. The study demonstrates that the alternate approach can produce reliable flood quantile estimates in regions with limited data, offering a promising solution for managing floods in these areas.

RESEARCH ON CHAOTIC CHARACTERISTICS AND SHORT-TERM PREDICTION OF EN-ROUTE TRAFFIC FLOW USING ADS-B DATA

The short-term traffic flow prediction can help to reduce flight delays and optimize resource allocation. Using chaos dynamics theory to analyze the chaotic characteristics of en-route traffic flow is the basis of short-term en-route traffic flow prediction and ensuring the orderly and smooth state of the en-route. This paper takes the time series of en-route traffic flow extracted from Automatic-Dependent Surveillance Broadcast (ADS-B) measured data as the research object, uses the improved C–C method to reconstruct the phase space, and uses the improved small data volume method to calculate the Lyapunov index to identify the chaos phenomenon of en-route traffic flow. In order to avoid the interference of chaos phenomenon on traffic prediction, the Wavelet Neural Network (WNN) model is established to predict the traffic flow at en-route points. The experimental shows that when the number of iterations is 10,000, the average accuracy of WNN prediction is 0.87173, and the average running time is 6.9335334[Formula: see text]s. According to the experimental results, it can be seen that the smaller number of iterations has more advantages in running time, which greatly reduces the overall running time. At the same time, it indicates that appropriately increasing or reducing the number of iterations in this experiment has little effect on the results.

ОЦЕНКА ЭКОНОМИЧЕСКОЙ УСТОЙЧИВОСТИ ОРГАНИЗАЦИЙ СЕЛЬСКОГО ХОЗЯЙСТВА

Studying the issues of sustainability and efficiency of enterprises is becoming increasingly important in the light of the emergence of periodic crises in the development of our country's economic systems, the consequences of which are difficult to predict. An increase in the stability of business entities can be achieved by introducing methods for assessing the volatility of time series of various indicators of an enterprise's activity. This approach will allow you to quickly identify critical areas that the organization's management should pay special attention to. The concept of volatility is often used to analyze stock and currency markets in order to study the risks associated with fluctuations in the value of assets. However, there are very few studies on the application of volatility in other areas, such as the sustainability and efficiency of agricultural enterprises, which may indicate insufficient study of this aspect. We suggest using volatility as an indicator to assess the economic sustainability and effectiveness of an organization. It is proposed to use the methodological apparatus for assessing the volatility of time series as an assessment of the sustainability of performance indicators of agricultural organizations, where the standard deviation for the time series of the indicator under study will be taken as a measure of deviation. The article discusses various types of volatility of financial time series: historical, expected and historical expected volatility. The formulas of their calculation and features are considered. An example of calculating volatility for time series of cash flows of an agricultural organization is given. The variability of cash flows can serve as an indicator of the economic stability of an agricultural organization under the influence of the external environment. This paper discusses a variety of techniques for measuring the volatility of time series. One of the proposed approaches involves the use of this method to assess the sustainability of cash flows of agricultural enterprises. The variability of cash flows can be considered as an indicator of the stability of an organization under the influence of the external environment.

CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland

Abstract. We present CAMELS-CH (Catchment Attributes and MEteorology for Large-sample Studies – Switzerland), a large-sample hydro-meteorological data set for hydrologic Switzerland in central Europe. This domain covers 331 basins within Switzerland and neighboring countries. About one-third of the catchments are located in Austria, France, Germany and Italy. As an Alpine country, Switzerland covers a vast diversity of landscapes, including mountainous environments, karstic regions, and several strongly cultivated regions, along with a wide range of hydrological regimes, i.e., catchments that are glacier-, snow- or rain dominated. Similar to existing data sets, CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes. CAMELS-CH (Höge et al., 2023; available at https://doi.org/10.5281/zenodo.7784632) encompasses 40 years of data between 1 January 1981 and 31 December 2020, including daily time series of stream flow and water levels, and of meteorological data such as precipitation and air temperature. It also includes daily snow water equivalent data for each catchment starting from 2 September 1998. Additionally, we provide annual time series of land cover change and glacier evolution per catchment. The static catchment attributes cover location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers. This Swiss data set complements comparable publicly accessible data sets, providing data from the “water tower of Europe”.

Domain-informed variational neural networks and support vector machines based leakage detection framework to augment self-healing in water distribution networks

The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed nations like the UK, which suffer from aging water infrastructure. Conventional models and analytical methods for detecting pipe leakage often face reliability issues and are generally limited to detecting leaks during nighttime hours. Moreover, leakages are frequently detected by the customers rather than the water companies. To achieve substantial reductions in leakage and enhance public confidence in water supply and management, adopting an intelligent detection method is crucial. Such a method should effectively leverage existing sensor data for reliable leakage identification across the network. This not only helps in minimizing water loss and the associated energy costs of water treatment but also aids in steering the water sector towards a more sustainable and resilient future. As a step towards ‘self-healing’ water infrastructure systems, this study presents a novel framework for rapidly identifying potential leakages at the district meter area (DMA) level. The framework involves training a domain-informed variational autoencoder (VAE) for real-time dimensionality reduction of water flow time series data and developing a two-dimensional surrogate latent variable (LV) mapping which sufficiently and efficiently captures the distinct characteristics of leakage and regular (non-leakage) flow. The domain-informed training employs a novel loss function that ensures a distinct but regulated LV space for the two classes of flow groupings (i.e., leakage and non-leakage). Subsquently, a binary SVM classifier is used to provide a hyperplane for separating the two classes of LVs corresponding to the flow groupings. Hence, the proposed framework can be efficiently utilised to classify the incoming flow as leakage or non-leakage based on the encoded surrogates LVs of the flow time series using the trained VAE encoder. The framework is trained and tested on a dataset of over 2000 DMAs in North Yorkshire, UK, containing water flow time series recorded at 15-minute intervals over one year. The framework performs exceptionally well for both regular and leakage water flow groupings with a classification accuracy of over 98 % on the unobserved test dataset

ОСОБЕННОСТИ ДОЛГОВРЕМЕННЫХ ФАЗ ПОВЫШЕННОЙ И ПОНИЖЕННОЙ ВОДНОСТИ ДОНА И ЛЕНЫ В XIX-XXI ВЕКАХ

The results of studying long-term (lasting 10-15 years or more) phases of decreased and increased conditionally natural annual and seasonal runoff of the Don River near the village of Razdorskaya and the Lena River near the village of Kyusyur are considered. The retrieval of long-term water flow time series (excluding the changes that are caused by anthropogenic impacts from the observed water flow) is based on the transformation of the annual hydrograph of average daily water flow using the Kalinin-Milyukov method. The long-term phases of annual and seasonal runoff have been identified on the basis of cumulative deviation curves and criteria for statistical homogeneity of time series by their averages. For the entire period of observations on the Don (1891-2019) and the Lena (1936-2019), two cardinally different types of long-term dynamics for contrasting phases of annual and seasonal runoff that are characteristic of these rivers and common in most of Russia have been revealed. On the Lena, the phases of decreased and increased values of annual and seasonal runoff have changed quasisynchronously, whereas on the Don, the phases of annual runoff and snow melt flood runoff on the one hand and summer-autumn and winter runoff on the other hand have changed asynchronously. The main characteristics of the contrast phases have been determined.

A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize the allocation of rail transit resources. However, the nonlinear and nonstationary nature of passenger flow time series challenges STPFP. To address this issue, a hybrid model based on time series decomposition and reinforcement learning ensemble strategies is proposed. Firstly, the improved arithmetic optimization algorithm is constructed by adding sine chaotic mapping, a new dynamic boundary strategy, and adaptive T distribution mutations for optimizing variational mode decomposition (VMD) parameters. Then, the original passenger flow data containing nonlinear and nonstationary irregular changes of noise is decomposed into several intrinsic mode functions (IMFs) by using the optimized VMD technology, which reduces the time-varying complexity of passenger flow time series and improves predictability. Meanwhile, the IMFs are divided into different frequency series by fluctuation-based dispersion entropy, and diverse models are utilized to predict different frequency series. Finally, to avoid the cumulative error caused by the direct superposition of each IMF’s prediction result, reinforcement learning is adopted to ensemble the multiple models to acquire the multistep passenger flow prediction result. Experiments on four subway station passenger flow datasets proved that the prediction performance of the proposed method was better than all benchmark models. The excellent prediction effect of the proposed model has important guiding significance for evaluating the operation status of urban rail transit systems and improving the level of passenger service.

Hydrological Regime Alteration Assessment in the Context of WFD 2000/60: A European and Global Review

Although the impact of hydrology on the ecological status of surface water bodies has been highly recognised, the hydrological regime alteration assessment has proven to be a challenging task. In this context, an extensive structured review analysis was used as a research method to investigate the strength and limitations of the hydrological regime alteration assessment methods as adopted by each member of the European Environment Agency and the cooperating countries, according to the Water Framework Directive 2000/60, as well as to propose future directions. The review was also widened to include the methods currently used worldwide in the hydrological alteration studies and the supporting software tools developed. The implementation of a common methodology on a European scale is not applicable, since a single approach would not be able to cope with the regional needs and conditions. The main limitation in almost all the methods developed by European countries and worldwide is the need for a flow time series of high temporal resolution, so as to also capture the systems’ extreme high and low flows. Automatic monitoring systems for rivers can provide a solution. Additionally, hydrological modelling may provide the necessary data for the definition of the reference conditions. Nevertheless, the main limitations of the methodologies reviewed and the challenge for future development are the incorporation of the groundwater contribution to the hydrological regime and the development of quantitative relationships between flow alteration and ecological response.

Evaluating stochastic rainfall models for hydrological modelling

Stochastic rainfall models are important tools for evaluating hydrological risks such as flooding and drought because of their ability to randomly generate alternative plausible climatic timeseries. The stochastic generation of climatic timeseries is not an end in itself, since they are typically applied to a catchment to determine the performance of water-related infrastructure systems, such as reservoirs or flood-control measures. This methodology typically involves a train of models to determine the end-of-system impact, yet the evaluation of stochastically generated rainfall timeseries is usually a stand-alone procedure focused on metrics directly related to the stochastic generator. This paper demonstrates discrepancies in this approach by evaluating two, daily-timestep, stochastic rainfall models in terms of rainfall metrics and their subsequently generated flow metrics after rainfall-runoff transformation. The two models are a Markov-based model and a latent-variable model, where each model is calibrated and evaluated showing ‘overall good’ performance. Stochastically generated timeseries, alongside observed rainfall timeseries are inputted to a calibrated catchment model (GR4J) to derive daily flow timeseries. Whereas the rainfall metrics typically showed ‘good’ performance, streamflow-based metrics are not necessarily ‘good’. The procedure is repeated for 277 stations from Australia and 106 stations from the United States of America. Depending on the strictness of the flow-based comparison and region analysed, using the Markov-based model 12–26% of sites were classified as ‘poor’ performing, and 1%-9% of sites were classified as ‘poor’ using the latent-variable model. The results demonstrate that catchment-based performance of flow metrics is more holistic since it magnifies features of the rainfall not otherwise visible to rainfall-based evaluation.

Variability and trend detection of water discharge over the past 60 years in the Vietnamese Hieu River Basin

Abstract This paper presents an in-depth analysis of the variability and trend detection of water discharge in the Hieu River Basin over the period from 1/1/1961 to 31/12/2020. Firstly, the wavelet analysis is implemented to investigate the flow variability at different time scales. Using time series of daily flow at Quy Chau and Nghia Khanh, the periodicities of flow are examined, showing that periodic oscillations in flow mainly occurred at 1 year, from 2 to 4 years, and from 4 to 8 years. Secondly, discharge variability is investigated, revealing an increase from January to September (excluding June) and a decrease from October to December. At Quy Chau, flow increased seasonally and annually by 0.19 and 0.06 m3/s, respectively, while its values at Nghia Khanh decreased of up to −0.50 m3/s. Thirdly, temporal trends of flow are assessed using Mann–Kendall and Sen's slope estimator, and sequential Mann–Kendall test. Results show flow decreases during 1961–1969, 1973–1976, and 1996–2020 and increases in 1969–1973 and 1976–1996. Temporal patterns of flow at Quy Chau and Nghia Khanh demonstrated synchronization across different time scales. Relationship between flow and rainfall is discussed to investigate insights into their relationship.

The Role of Geomorphology on Flood Propagation in a Large Tropical River: The Peculiar Case of the Araguaia River, Brazil

In large rivers, floods are affected by the mosaic of geomorphic and geologic settings of the fluvial corridor. Here, we assess the role of geomorphology on the downstream flood dynamics of the Araguaia River, the largest free-flowing river in Central Brazil. The study integrates and advances existing flood-type classifications. We assess the factors that govern flood hydrograph properties and their downstream propagation by using flow time series, conducting statistical analysis, and evaluating geomorphic and flood metrics. Our findings highlight the role of geomorphology in the transmission of floods. In the upper and lowermost fluvial segments, the geological characteristics of the valley are a major factor. In the intermediate section, two main factors modulate the floods. The wide and complex floodplain plays a major role through storage and buffer effect for floods, and water diversion from the main system to a huge abandoned channel by avulsion governs seasonal flow transfers. The Araguaia is the most geodiverse floodplain of the Amazon–Cerrado ecotone, and floods play a fundamental ecological role in the river–floodplain environments. The combination of diverse factors controlling the flood mechanisms has to be considered when implementing conservation plans for the fluvial corridor and effective river management strategies.

Effect of dam construction on changes in river's environmental flow (case study: Gorganrood river in the south of the Caspian Sea)

Human activities such as dam construction can lead to significant river flow regime changes and, consequently, river environments and habitats. Different approaches for estimating the environmental flow have developed to improve the river ecosystem's environmental and habitat conditions. The hydrological approach is considered in many studies to estimate environmental flows considering its advantages. Vimba vimba persa is an economically and ecologically valuable species in the Caspian Sea that spawns and reproduces naturally in the Gorganrood River downstream in April–May. After the construction of the Voshmgir dam on the Gorganrood River, the fish population has drastically reduced in recent decades. In this study, to investigate the effect of constructing the Voshmgir dam on the environmental flow of Gorganrood River, a wide range of hydrological methods, including Tennant, Tessman, Arkansas, Aquatic Base Flow, Flow Duration Curve, High Flow-Low Flow, and FDC Shifting have been used. Comparing the correlation between the mean monthly flow time series before and after the Voshmgir dam demonstrates remarkable changes, including Pearson (0.95 to 0.86), Spearman's rho (0.93 to 0.78), and Kendall's τ (0.78 to 0.61) as a consequence of dam construction. The results indicate that estimated environmental flow from Tennant (1.1, 3.4 to 1.3, 3.8 cms) and Aquatic Base Flow (0.5 to 1.8 cms) methods have led to inadequate estimation due to significant changes in flow in different months. The amount of environmental flow during April–May estimated from investigated methods indicates the dam negatively affected water allocation to meet the river downstream's ecological and environmental needs. The flow duration curve analysis confirms that the dam construction has caused the river flow to fluctuate significantly within the year. The dam construction has dramatically reduced the flow in April–May, a critical environmental and ecological season. The April–May period coincides with the river's flood season. The construction of the Voshmgir dam has caused the components of high flow in the environmental flow to face severe shortages.

Application of the visual boundary recurrence plots to magnetohydrodynamic turbulence channel flow

The visual boundary recurrence plot (VBRP) method is used in this analysis to study time series of fluctuating velocities in turbulent magnetohydrodynamic channel flow that were studied in our previous study using the theory of the recurrence plots. The VBRP is a new graphical tool, which visualizes each point of the recurrence plot with colors depending on whether the neighboring points are recurrent or not and enhances, thus, the study of the stability of the textures of the recurrence plots, viewing them as geometrical shapes. We calculate the VBRPs of the turbulent velocity time series of a simulated channel flow monitored at constant streamwise and spanwise positions and at a number of positions along the wall-normal direction. We further estimate the visual boundary recurrence plot rates to indicate the change of the dynamics of the flow. The results highlight the flow dynamics under the influence of the magnetic field and identify additional correlations/similarities between the flow point positions within the channel. In this study, we show that these areas correspond to perturbations due to strikes that are created close to the boundary and move toward the centerline. It also seems that we have more often creation of streaks in the case of absence of magnetic field.

Combining Landsat TIR‐imagery data and ERA5 reanalysis information with different calibration strategies to improve simulations of streamflow and river temperature in the Canadian Subarctic

AbstractArctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological‐water temperature model to derive consistent long‐term daily flow and stream temperature time series in Aux Mélèzes River, a non‐regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth‐generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single‐site, multi‐site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling‐Gupta Efficiency (KGE) metric >0.8. Using the best‐performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi‐site calibration approach being the most accurate with root mean square errors (RMSE) <2.0°C. The validation of the water temperature simulations with a three‐year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data‐sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada.

Insights into post-emplacement lava flow dynamics at Mt. Etna volcano from 2016 to 2021 by synthetic aperture radar and multispectral satellite data

Insights into post-emplacement lava flow dynamics at Mt. Etna volcano from 2016 to 2021 by synthetic aperture radar and multispectral satellite data

Flow forecasting for leakage burst prediction in water distribution systems using long short-term memory neural networks and Kalman filtering

Reducing pipe leakage is one of the top priorities for water companies, with many investing in higher quality sensor coverage to improve flow forecasting and detection of leaks. Most research on this topic is focused on leakage detection through the analysis of sensor data from district metered areas (DMAs), aiming to identify bursts after their occurrence. This study is a step towards the development of ‘self-healing’ water infrastructure systems. In particular, machine learning and deep learning-based algorithms are applied to forecasting the anomalous water flow experienced during bursts (new leakage) in DMAs at various temporal scales, thereby aiding in the health monitoring of water distribution systems. This study uses a dataset of over 2,000 DMAs in North Yorkshire, UK, containing flow time series recorded at 15-minute intervals for a period of one year. Firstly, the method of isolation forests is used to identify anomalies in the dataset, which are cross referenced with entries in the water mains repair log, indicating the occurrence of bursts. Going beyond leakage detection, this research proposes a hybrid deep learning framework named FLUIDS (Forecasting Leakage and Usual flow Intelligently in water Distribution Systems). A recurrent neural network (RNN) is used for mean flow forecasting, which is then combined with forecasted residuals obtained through real-time Kalman filtering. While providing expected day-to-day flow demands, this framework also aims to issue sufficient early warning for any upcoming anomalous flow or possible leakages. For a given forecast period, the FLUIDS framework can be used to compute the probability of flow exceeding a pre-defined threshold, thus allowing decision-making for any necessary interventions. This can inform targeted repair strategies that best utilize resources to minimize leakages and disruptions. The FLUIDS framework is statistically assessed and compared against the state-of-practice minimum night flow (MNF) methodology. Based on the statistical analyses, it is concluded that the proposed framework performs well on the unobserved test dataset for both regular and leakage water flows.

A Chaotic Discriminant Algorithm for Arrival Traffic Flow Time Series Based on Improved Alternative Data Method

<p>Chaos discrimination is a prerequisite for the application of chaos theory modeling. Since the average orbital period of an air traffic flow system is long, it is difficult to obtain time series with a small time scale and many data points, so the Small-Data Method is often adopted to quantitatively calculate the chaotic characteristic quantity. However, when using the power spectrum method, it is found that the Small-Data Method is prone to false judgments when the data volume is small. To reduce spurious judgments, we apply a chaos discrimination algorithm based on an Improved Alternative Data Method combined with the Small-Data Method for air traffic flow and analyze it by example. The algorithm was experimentally demonstrated to correct the false judgment results of the Small-Data Method. In particular, when the data volume is only 150, the discrimination accuracy of the improved algorithm is as high as 80%, which is 26% higher than the discrimination accuracy of the Small-Data Method. Moreover, the improved algorithm has better discriminative performance than the Small-Data Method under the same data volume condition, which is suitable for the chaotic discriminative analysis of the arrival traffic flow time series.</p> <p> </p>

Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks.

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies.

Dynamic Metro Stations Importance Evaluation Based on Network Topology and Real-Time Passenger Flows

Identifying the importance of metro stations is crucial for enhancing the efficiency and safety of the urban rail transit system. This study proposes an integrated approach to evaluate the importance of metro stations based on jointly the static metro network topology and the dynamic real-time passenger flows. The static characteristics of metro stations are reflected through node degree and clustering coefficient of metro network topology based on the complex network theory. The dynamic characteristics of metro stations are assessed using the passenger flow time series, which were modeled using the autoregressive integrated moving average plus autoregressive conditional heteroscedasticity model (i.e., ARIMA+GARCH model). By combining the static and dynamic characteristics of metro stations, a comprehensive index is proposed to quantify the importance of metro stations. The integrated approach is tested using metro network and passenger flow data collected from Suzhou, China. The results indicate that compared with the topology analysis, the passenger flows could strengthen the importance of transfer stations and their neighboring stations. Especially for the loop lines that formed by the intersection of two or more lines within the network, the importance index of metro stations within these loop lines is inversely proportional to the number of stations in the loop. It indicates that as a metro network continues to expand with the addition of new lines and stations, this is a high likelihood of increased concentration of importance with the network. The concentration of importance in the metro network can be attributed to the emergence of highly connected loop lines that facilitate efficient movement of passengers.