City Network Evolution Characteristics of Smart Industry: Evidence from Yangtze River Delta, China

The dual-path model of industrial evolution and spatial progression has been widely acknowledged and incorporated into the strategic planning to promote the development of urban industries and regional collaborations.However,current research on inter-enter-prise city networks mainly focuses on the single sector of flows on all enterprise branches,such as product value chains and production factors,but neglects that of particular industry department.Built upon the new economic geography and city networks theory,this paper develops a methodological framework that focuses on the analysis of city network evolution characteristics of smart industry.Particu-larly,a conceptual model of smart industry enterprise-industry-city is proposed and then applied to a case study of smart industry in the Yangtze River Delta Region,China.Using enterprise supplier-customer data,a city network of smart industry is constructed and sub-sequently analyzed with the proposed model.Findings indicate that the smart industry network in Yangtze River Delta Region exhibits a hierarchical structure and the expansion of the network presents a small-world network characteristic.The study not only makes a meth-odological contribution for revealing the industrial and spatial evolution path of the current smart industry,but also provides empirical support for the formulation of new economic development policies focused on smart industries,demonstrating the role of city clusters as carriers of regional synergistic development.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

Ecological Network Resilience of Shiyang River Basin: An Arid Inland Watershed of Northwest China

Due to the uncertainties posed by climate change,resilience has become an increasingly important variable for evaluating regional ecosystem stability.The assessment of Ecological Network Resilience(ENR)is crucial for establishing mitigation strategies and sustainable socioeconomic development in arid regions.Shiyang River Basin is an arid watershed in Northwest China with complex characteristics,its ENR and spatial differentiation characteristics in 2020 were investigated in this work based on the Complex Adaptive System(CAS)theory.The results indicated that the mean Ecological Network Resilience Index(ENRI)value for the Shiyang River Basin was 0.390 in 2020,and the mean values in the southern mountainous,middle oasis,and northern desert regions of the basin were 0.598,0.461,and 0.237,respectively,demonstrating evident spatial differences.Additionally,the ENR of the basin exhibited distinct distribution characteristics across different dimension,whereas the trend of the integrated ENR of the basin was consistent with that of elemental,structural,and functional resilience,indicating the constructed three-region ENR model based on the logical relationship of element-structure-function was suitable for evaluation of the ENR in arid inland river watersheds.Furthermore,strategies for enhancing regional ENR were proposed from the perspective of adapting to climate change.

Study on the Effects and Influencing Factors of the Economic Spatial Correlation Network in the Yellow River Basin

This paper constructs high-quality development assessment indicators based on the perspective ternary system,including economic development,technological innovation,and ecological environment systems.Based on the data of 51 regions in the Yellow River Basin from 2010 to 2021,the economic spatial correlation relationships were constructed.By using social network analysis and the QAP method,the economic spatial correlation characteristics and the influencing factors are deconstructed.The results show that:1)The regions exhibit significant variations in comprehensive quality and economic connectivity.Zhengzhou,Xi’an,Jinan,Luoyang,and Zibo are the top five regions.Regions with high comprehensive quality tend to have stronger economic ties.The economic links show an obvious“upstream-midstream-downstream”three-tier structure.2)Regions such as Xi’an,Zhengzhou,Jinan,Taiyuan,Ordos,Luoyang,Baotou etc.,exhibit high degree and betweenness centrality,and low closeness centrality.Those are the core regions of high-quality development in the Yellow River Basin.Block I is the core block and spills to Block II,Block III,and Block IV.Block II plays an essential bridge role to Block III.3)The factors of spatial adjacency,fixed asset investment,employment,informatization,and innovation are key to spatial correlation,and explain 40.5%of the spatial correlation.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

Relationship Between Changes of River-lake Networks and Water Levels in Typical Regions of Taihu Lake Basin,China

The typical regions of the Taihu Lake Basin,China,were selected to analyze the variation characteristics of river-lake networks under intensive human activities.The characteristics of the fractal dimension of river networks and lakes for different periods were investigated and the influences of river system evolution on water level changes were further explored through the comparison of their fractal characters.The results are as follows:1) River network development of the study area is becoming more monotonous and more simple;the number of lakes is reducing significantly,and the water surface ratio has dropped significantly since the 1980s.2) The box dimension of the river networks in all the cities of the study area decreased slowly from the 1960s to the 1980s,while the decrease was significant from the 1980s to the 2000s.The variations of lake correlation dimension are similar to those of the river network box dimensions.This is unfavorable for the storage capacity of the river networks and lakes.3) The Hurst exponents of water levels were all between 0.5 and 1.0 from the 1960s to the 1980s,while decreased in the 2000s,indicating the decline in persistence and increase in the complexity of water level series.The paper draws a conclusion that the relationship between the fractal dimension of river-lake networks and the Hurst exponents of the water level series can reveal the impacts of river system changes on flood disasters to some extent:the disappearance of river networks and lakes will increase the possibility of flood occurrence.

Articulating China's Science and Technology:Knowledge Collaboration Networks Within and Beyond the Yangtze River Delta Megalopolis in China

In this paper, we reconsider the defining but often overlooked ‘hinge' function of megalopolises by analyzing how megalopolises have articulated national and international urban systems in the context of a globalizing knowledge economy. Taking the case of China's Yangtze River Delta(YRD) region, we particularly focus on knowledge circulation within and beyond the YRD region by analyzing the pattern and process of knowledge collaboration at different geographical scales during the 2004–2014 period. Results show that the structure of scientific knowledge collaboration as reflected by co-publications has been strongest at the national scale whereas that of technological knowledge collaboration as measured by co-patents has been strongest at the global scale. Despite this difference, the structure of both scientific and technological knowledge collaboration has been functionally polycentric at the megalopolitan scale but become less so at the national and global scales. The ‘globally connected but locally disconnected' pattern of Shanghai's external knowledge collaboration suggests that the gateway role of the YRD megalopolis in promoting knowledge collaboration at different geographical scales will take time before it is fully realized.

Distribution characteristics and controlling factors of typical antibiotics and antibiotic resistance genes in river networks in western area of Wangyu River,China

Antibiotics and antibiotic resistance genes(ARGs)pose health risks in aquatic environments because of their persistence and mobility.River networks can provide a perfect opportunity for exploring the occurrence and enrichment of ARGs and antibiotics in freshwater environments.On this basis,the abundances of four types of antibiotics(sulfonamides,quinolones,tetracyclines,and macrolides)and 13 ARGs(sulⅠ,sulⅡ,tetA,tetB,tetO,tetW,qnrA,qnrS,qnrD,ermB,ermF,ermC,and ere A)were measured in the river networks of the west bank of the Wangyu River in China.The spatial distribution and temporal variation of these antibiotics and ARGs were characterized,and their controlling factors were analyzed.All four types of antibiotics were detected with high frequencies between 41%and 100%.Quinolone antibiotics exhibited the highest average concentration(286.53 ng/L).The concentrations of quinolones,tetracyclines,and macrolides were significantly higher in the winter than in the summer,whereas the concentration of sulfonamides was higher in wet periods than in dry periods.Of the 13 ARGs,sulⅠwas the most abundant(1.28 x 10^(5)copies per milliliter),followed by sulⅡand tetO(5.41×10^(4)and 4.45×10^(4)copies per milliliter,respectively).The canonical correspondence analysis showed that environmental factors,including dissolved oxygen,water temperature,total nitrogen,pH,and total phosphorus,had significant effects on the abundance of ARGs.sulⅠ,sulⅡ,tetA,and tetB were significantly correlated with 16S ribosomal RNA sequences,indicating that the bacterioplankton community might affect the distribution of ARGs.The correlation heat map analysis showed that the spread of ARGs was influenced by specific bacterial groups,such as Acidobacteria and Cyanobacteria,indicating that these bacterioplankton may be the hosts of environmental ARGs.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

Time Series Modeling of River Flow Using Wavelet Neural Networks

A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river flow. The time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analyzed by the WNN model. The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables. The hybrid model (WNN) was compared with the standard ANN and AR models. The WNN model was able to provide a good fit with the observed data, especially the peak values during the testing period. The benchmark results from WNN model applications showed that the hybrid model produced better results in estimating the hydrograph properties than the latter models (ANN and AR).

Project of “Three Networks Greening” based on optimal allocation in the Yellow River Delta,China (Dongying section)

We have used the Yellow River Delta （Dongying section） as our study area to address the project of ＂Three Networks Greening＂ （TNG）. With the use of GIS technology and from an ecological point of view, an optimal allocation scheme of land resources is constructed and applied to guide the adjustment of land resources. Given this scheme, we have calculated that the area of land suitable for forest and shrubs without greening is 2256 km^2. Simultaneously, acting on the layout of the TNG project, afforestation site types are prepared and improved. Soil types, microrelief, salinity and underwater levels are combined as major classification factors and irrigation conditions as a reference to classify sites into eight types. In this way, land suitable for forest and grass is afforested given particular planting patterns. Finally, by overlaying this forestry site type map with the TNG plan map, some suggestions and strategies are proposed and used to direct the TNG project. An ecological oasis of the Yellow River Delta should be the result.

Prediction of discharge in a tidal river using the LSTM-based sequence-to-sequence models

The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal rivers.Long short-term memory(LSTM)networks excel in processing and predicting crucial events with extended intervals and time delays in time series data.Additionally,the sequence-to-sequence(Seq2Seq)model,known for handling temporal relationships,adapting to variable-length sequences,effectively capturing historical information,and accommodating various influencing factors,emerges as a robust and flexible tool in discharge forecasting.In this study,we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River(Yangtze River)Estuary.This study focuses on discharge forecasting using three key input characteristics:flow velocity,water level,and discharge,which means the structure of multiple input and single output is adopted.The experiment used the discharge data of the whole year of 2020,of which the first 80%is used as the training set,and the last 20%is used as the test set.This means that the data covers different tidal cycles,which helps to test the forecasting effect of different models in different tidal cycles and different runoff.The experimental results indicate that the proposed models demonstrate advantages in long-term,mid-term,and short-term discharge forecasting.The Seq2Seq models improved by 6%-60%and 5%-20%of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction,respectively.In addition,the relative accuracy of the Seq2Seq model is 1%to 3%higher than that of the LSTM model.Analytical assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle well.This indicates the significance of the Seq2Seq models.

Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

Mapping Urban Networks through Inter-Firm Linkages: The Case of Listed Companies in Yangtze River Delta, China

Recently, literature on urban network research from the perspective of ?rm networks has been increasing. This research mainly used data from the headquarters and branches of all 2581 listed manufacturing companies in the Yangtze River Delta from 1990 to 2017, and studied the urban network through an interlocking network model that quantifies the links between enterprises. The results showed that the spatial distribution of listed manufacturing industries in the Yangtze River Delta was relatively concentrated, and cities such as Shanghai, Nanjing, and Hangzhou were hot spots for the spatial distribution of listed manufacturing industries. However, Fuyang, Suqian, Chizhou, Lishui and other network edge cities were less distributed in manufacturing. The urban network of the Yangtze River Delta has significant hierarchical characteristics. The urban network of the Yangtze River Delta presents a multi-center network development mode with Shanghai as the center and Nanjing, Hangzhou, and Hefei as the sub-centers. Moreover, we found that the development of inter-city connections in the Yangtze River Delta was driven by network mechanisms of priority attachment and path dependence. The radiating capacity and agglomeration capacity of cities in the Yangtze River Delta have a strong polarization characteristic. The core cities such as Shanghai, Nanjing, Hangzhou, and Hefei have much higher network radiation capabilities than network aggregation capabilities. However, other non-core cities and network edge cities have weak network radiation capabilities, and mainly accept network radiation from core cities. It enriches the research of urban networks based on real inter-?rm connections, and provides ideas for the wider regional study and the combination of econometric techniques and social network analysis.

Application of Artificial Neural Networks in Order to Predict Mahabad River Discharge

Estimating of river discharge is one of the more important parameters in the water resources management. In recent years, due to increasing population, increased water consumption in industrial, agricultural and health sections, thus water shortge becomes a global problem. Accurate estimation of the river discharge is one of the most important parameters in surface water resources management, especially in order to determine appropriate values in flood, drought, drinking, agricultural and industral topics. The case study in this research is Mahabad River that is located in west Azarbaijan province in west north of Iran. In this study, we used 70%, 15% and 15% data in order to train, validate and test, respectively. In this study, data of Kawtar and Baitas stations were used in order to determine Mahabad River discharge. In each ststion, several different networks were prepared using NeuroSolutions V.6.0 software. The neural models included Multilayer Perceptron (MLP), Generalized Feed Forward, Jordan/Elman, Radial Basis Functions (RBF) and Principle Component Analysis (PCA), and different transfer functions included Tanh, Sigmoid, Linear Tanh, Linear Sigmoid and the number of hidden layers of.The different number of nodesin layers with different learning algorithms (Momentum, Levenberg Marquardt, Quickprop, DeltaBarDelta, Conjugate Gradient) and different networks were compared. The results showed the artificial neural networks. They predicted the river discharge with 10.67 and 0.94 (m3/s)2 and the high value of correlation coefficient with 0.88 and 0.75 for Kawtar and Baitas stations respectivly.

Sensitivity Analysis of Radial Basis Function Networks for River Stage Forecasting

Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.

The Research and Application of BP Neural Networks in River-basin Water and Sediment

Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.

基于MIKE HYDRO River模型的轨交交通施工期防汛安全影响论证

轨交交通施工期间,受车站结构设计、周边场地限制等因素影响,河道需要断流施工,断流会对区域的防洪除涝安全及水环境产生影响。本文以上海轨道交通13号线西延伸工程为例,构建一维河网水动力模型,分析排涝及引调水工况下水位及水质变化情况。结果表明,平原河网地区个别河道断流对于区域防洪除涝安全影响较小,会严重影响河道断流处的水环境;通过增设导流措施可以减轻断流的影响,为平原河网地区河道断流影响分析及措施制定提供依据。

River channel flood forecasting method of coupling wavelet neural network with autoregressive model

Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.

Regional accessibility of land traffic network in the Yangtze River Delta

In a given district, the accessibility of any point should be the synthetically evaluation of the internal and external accessibilities. Using MapX component and Delphi, the author presents an information system to calculate and analyze regional accessibility according to the shortest travel time, generating thus a mark diffusing figure. Based on land traffic network, this paper assesses the present and the future regional accessibilities of sixteen major cities in the Yangtze River Delta. The result shows that the regional accessibility of the Yangtze River Delta presents a fan with Shanghai as its core. The top two most accessible cities are Shanghai and Jiaxing, and the bottom two ones are Taizhou （Zhejiang province） and Nantong With the construction of Sutong Bridge, Hangzhouwan Bridge and Zhoushan Bridge, the regional internal accessibility of all cities will be improved. Especially for Shaoxing, Ningbo and Taizhou （Jiangsu province）, the regional internal accessibility will be decreased by one hour, and other cities will be shortened by about 25 minutes averagely. As the construction of Yangkou Harbor in Nantong, the regional external accessibility of the harbor cities in Jiangsu province will be speeded up by about one hour.