Calculating the return value using a mathematical model of significant wave height

This study presents a new method for calculating return values. The essence of the method is that it utilizes nonstationary data to calculate the return value for a region in the Northeast Pacific. The nonstationary data was obtained from a model which was previously developed for the behavior of the significant wave height as a function of time in the region. The method is illustrated by convolving two generalized Pareto distribution functions fitted to two parts of the model, computing a suitable extreme value from the new distribution function, and calculating the return value using this extreme value.     

环境监测工作中的数据处理和质量控制

为了保证环境监测数据的准确可靠，对环境监测中的数据处理及质量控制进行了初步阐述，并举例简要说明，以确保监测数据的准确性。可比性。     

Traffic consolidation in East Asian container ports: A network flow analysis

The proliferation of hub-and-spoke operations in maritime container transportation has resulted in the widespread consolidation of traffic flows. Utilising liner shipping network configurations, this paper assesses the impact of freight traffic consolidation in the container port industry by exploring the spatial pattern of traffic flow movements and identifying the variety of roles that container ports play within this context. On the basis of the network concept, the spatial inequality of freight traffic consolidation is determined by the density and direction of all meaningful connections (i.e. significant flows) identified by applying Multiple Linkage Analysis (MLA) to an initial traffic flow matrix.The effectiveness of the chosen methodology is tested empirically using a sample comprising the 18 major container ports in East Asia, together with another 21 important container ports located on the East–West trading route. Based on this sample network, the spatial structure of traffic flow consolidation reveals the nature and structure of hub-and-spoke operations within a port system, the relative hub-dependence of ports, the variety of roles which individual ports play within the overall structure of inter-port interactions and the hierarchical configuration of the port industry structure. The paper concludes that MLA offers new insights into the distributional inequality of traffic flows, the spatial and economic interactions between ports and the extent to which hinterlands overlap. Furthermore, the analysis clearly shows that inter-port relationships can no longer be evaluated as isolated phenomena; any change in a specific port’s competitiveness will directly impact upon the structure of the whole maritime transportation system. Port authorities and terminal operators will need, therefore, to carefully analyse and disentangle specific inter-port relationships in order to provide the most appropriate basis for their decision making.     

An algorithm to discover spatial–temporal distributions of physical seawater characteristics and a case study in Turkish seas

Clustering is one of the major data mining methods to obtain a number of clues about how the physical properties of the water are distributed in a marine environment. It is a difficult problem, especially when we consider the task for spatial–temporal marine data. This study introduces a new clustering algorithm to discover regions that have similar physical seawater characteristics. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to the nonspatial, spatial, and temporal values of the objects. Our algorithm also overcomes three drawbacks of existing clustering algorithms: problems in the identification of core objects, noise objects, and adjacent clusters. This paper also presents a spatial–temporal marine data warehouse system designed for storing and clustering physical data from Turkish seas. Special functions were developed for data integration, data conversion, querying, visualization, analysis, and management. User-friendly interfaces were also developed, allowing relatively inexperienced users to operate the system. As a case study, we show the spatial–temporal distributions of sea surface temperature, sea surface height residual, and significant wave height values in Turkish seas to demonstrate our algorithm.