Zhang Ren

The spaceborne platform has unprecedently provided the global eddy-permitting (typically about 0.25°) products of sea surface salinity (SSS), however the existing SSS products can hardly resolve mesoscale motions due to the heavy noises therein and the over-smoothing in denoising processes. By means of the multi-fractal fusion (MFF), the high-resol...

Estimating subsurface thermohaline structure from concurrent satellite data is a meaningful way to enrich internal oceanic observations. As a powerful tool for data mining, many studies have used machine learning in subsurface reconstruction, but most conventional applications have been purely black-box in nature without further consideration of oc...

Sea surface salinity (SSS) is one of the most important variables in ocean dynamics and atmospheric climate. The launch of three salinity satellites, Aquarius, SMAP and SMOS, has greatly expanded the global sea surface salinity data field. The latest ocean surface salinity (CCI+SSS) fusion project fully utilizes the satellite data from SMOS, supple...

The thermocline, as a special structure in the ocean temperature field, has an important impact on sound propagation, material transport, and human underwater activities in the ocean. Using existing methods, the thermocline is distributed underwater and cannot be observed in real-time and with high spatiotemporal accuracy. It can only be calculated...

Artificial intelligence (AI) has been a hot research topic in recent years and the algorithm is its technical core. Many new algorithms have been proposed to solve complex non-linear optimization problems, and each of them has its own advantages and disadvantages. This study improves the selection operator of Genetic Algorithm (GA) by combining art...

With the accelerated melting of the Arctic sea ice, the opening of the Northeast Passage of the Arctic is becoming increasingly accessible. Nevertheless, the constantly changing natural environment of the Arctic and its multiple impacts on vessel navigation performance have resulted in a lack of confidence in the outcomes of polar automated route p...

Vulnerability research and ecological risk assessment of coastal ecological environment are of great significance for the temporal and spatial evolution process and trend of coastal zone. In this paper, using the 4-phase GF-1 remote sensing data, the landscape fragility index and landscape artificial disturbance intensity index of the Shanghai sect...

A series of problems such as climate change, population explosion and energy crisis make people have to focus on the development of new energy. The 21st Century Maritime Silk Road region, with its large population and well-developed industry, is well suited for the development of new energy, especially offshore wind energy resources. Aiming at the...

The navigational potential of the Arctic shipping routes is gradually emerging under the trend of melting Arctic sea ice. However, the opening of the Arctic shipping routes still faces many difficulties, especially the complexity of sea ice changes and the navigational safety risks caused by the uncertainty of the sea ice forecast. In recent years,...

This paper presents a new framework for economic analysis of marine transportation, where uncertainty is given more attention than in the current research. A new quantitative method combined with the importance ranking method is used to access the uncertainty of evidence and for fusion of evidence from multiple sources. The Bayesian network model i...

Carbon emission reduction, an effective way to facilitate carbon neutrality, has gained increasing attention in government policy and scientific research. However, the establishment of a sustainable carbon emission reduction market is a complex game between governments and enterprises. In addition, it is difficult to obtain precise evaluations of t...

As ocean environment is complicated and varied, underwater vehicles (UVs) are facing great challenges in safe and precise navigation. Therefore, it is important to evaluate the underwater ocean environment safety for the UV navigation. To deal with the uncertain knowledge and various information in the safety assessment, we present an evaluation mo...

Offshore wind energy has become a hot spot in new-energy development due to its abundant reserves, long power generation time, high unit capacity and low land occupation. In response to the current situation whereby wind energy, and natural and human factors have not been taken into account in the selection of sites for offshore wind-energy-resourc...

The maritime silk road policy of China brings opportunities to companies relating to overseas investment. Despite the investment potentials, the risks cannot be ignored and have still not been well assessed. Considering the fact that ICRG comprehensive risk has certain subjectivity, it is not completely applicable to China’s overseas investment. Th...

Risk assessment and management of marine disasters are the prerequisite of ocean exploitation and utilization. Marine disaster assessment is a complicated system engineering with high non-linearity and uncertainty. To deal with the problem, Bayesian network (BN) has become a powerful model used for disaster assessment due to its capability of expre...

The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simp...

Conventional methods to reconstruct ocean interior temperature/salinity (T/S) from surface data are mostly “pure data‐driven.” On the other hand, the reconstruction methods based on surface quasi‐geostrophic (SQG) dynamics present promising results in retrieving mesoscale density structures. It is rarely considered to incorporate SQG‐based reconstr...

The navigability potential of the Northeast Passage has gradually emerged with the melting of Arctic sea ice. For the purpose of navigation safety in the Arctic area, a reliable daily sea ice concentration (SIC) prediction result is required. As the mature application of deep learning technique in short-term prediction of other fields (atmosphere,...

The navigability potential of the Northeast Passage has gradually emerged with the melting of Arctic sea ice. For the purpose of navigation safety in the Arctic area, a reliable daily sea ice concentration (SIC) prediction result is required. As the mature application of deep learning technique in short-term prediction of other fields (atmosphere,...

It is necessary to evaluate satellite sea surface salinity (SSS) L3 products prior to using them to analyze SSS variability. Instead of performing comparison analysis on the accuracy of products (e.g., Root Mean Square Deviation (RMSD)), two new evaluation methods, information entropy and local variance, are introduced to assess the performance of...

Based on Bayesian network (BN) and information flow (IF), a new machine learning-based model named IFBN is put forward to interpolate missing time series of multiple ocean variables. An improved BN structural learning algorithm with IF is designed to mine causal relationships among ocean variables to build network structure. Nondirectional inferenc...

There are two challenges in the comprehensive marine hazard assessment. The influencing mechanism of marine disaster is uncertain and disaster data are sparse. Aiming at the uncertain knowledge and small sample in assessment modeling, we combine the information diffusion algorithm and Bayesian network to propose a novel assessment model. The inform...

Marine environments have a considerable influence on the construction of the Chinese 21st Century Maritime Silk Road. Thus, an objective and quantitative risk assessment of marine environments has become a key problem that must be solved urgently. To deal with the uncertainty in marine environmental risks caused by complex factors and fuzzy mechani...

Accurate and fast prediction of sea ice conditions is the foundation of safety guarantee for Arctic navigation. Aiming at the imperious demand of short-term prediction for sea ice, we develop a new data-driven prediction technique for the sea ice concentration (SIC) combined with causal analysis. Through the causal analysis based on kernel Granger...

This study employs three remotely sensed Sea Surface Salinity products to diagnose the “inconsistent pairs” between the in situ observations of the Near Surface Salinity from delayed‐mode tropical moored buoys and Argo floats and the satellite salinity in the temporal range of April 2015–December 2018. By means of an adapted 3‐Sigma criterion and u...

This study aims to examine the feasibility of the Northeast Passage for container transportation during the period of 2020–2030. A novel model is developed to study the impact of sea-ice conditions on freight rate between the Northeast Passage and Suez Canal Route on a round voyage. The sea-ice concentration and thickness are treated as key factors...

The Arctic warming has become a key signal with global climate change in recent decades. In this study, sea ice volume and whole layer atmospheric heat flux divergence were used to represent the local factor and external transport, respectively. The random forest algorithm was adopted to study the nonlinear effects and variations in importance betw...

To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study. Previously, similar deep learning algorithms (such as convolutional neural networks; CNNs) were frequently us...

Background and motivation The distance measure is a classical topic in the intuitionistic fuzzy set theory. Although plenty of distance measures have been proposed and successfully applied to the decision-making problems, it is found that there still exists the counter-intuitive phenomenon where the context information in the alternatives is seldom...

Although salinity satellites can provide high-resolution global sea surface salinity (SSS) data, the satellite data still display large errors close to the coast. In this paper, a nonlinear empirical method based on random forest is proposed to correct two Soil Moisture and Ocean Salinity (SMOS) L3 products in the tropical Indian Ocean, including S...

The Bayesian Network (BN) has been widely applied to causal reasoning in artificial intelligence, and the Search-Score (SS) method has become a mainstream approach to mine causal relationships for establishing BN structure. Aiming at the problems of local optimum and low generalization in existing SS algorithms, we introduce the Ensemble Learning (...

The preferential use of renewable energy sources such as wind power has been proposed as one of the most effective strategies in reducing greenhouse gas emissions in the energy sector. However, wind energy resources are vulnerable to climate change, which might have a huge impact on the area under consideration. In this research, we used the wind s...

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This paper aims to find a possible ensemble method to combine the global climate models, providing an accuracy forecast of sea ice thickness. Conventional multimodel superensemble, the advanced method that is widely used in atmosphere, ocean and other fields, cannot be well performed in sea ice thickness simulation. Hence, an adaptive forecasting t...

The spatial and temporal decorrelation scales of sea surface salinity (SSS) have been calculated in the tropical Indian ocean from the satellite measurements including Soil Moisture and Ocean Salinity (SMOS), Aquarius, Soil Moisture Active Salinity (SMAP), and the model output data for the period of 2011–2017. The differences in spatial and tempora...

Abstract. This study proposes adaptive forecasting through exponential re-weighting based on the Structural Similarity Index Measure (AFTER-SSIM) algorithm to evaluate the performance of global climate models from the Coupled Model Intercomparison Project (CMIP5) under different emission scenarios during 2006 to 2018, attempting to reduce the uncer...

Over the past decade, terrorism risk has become a prominent consideration in protecting the wellbeing of individuals and organizations. Consequence assessment of terrorist attack has become a research hotspot in security science. Aiming at the multi-source, interactional and uncertain factors in terrorism events, we introduce Information Flow (IF)...

Recent global warming has made it possible to exploit and utilize resources in the Arctic Northwest Passage. However, the harsh natural environment in this sea area poses a major threat to safety during navigation. Although this passage is extensive, the natural environmental state of few key nodes affect the navigability of the entire passage. In...

Combining the dynamical surface‐trapped mode derived from the Surface Quasi‐Geostrophic (SQG) function with the statistical mode calculated from multivariate empirical orthogonal function (EOF) reconstruction (mEOF‐R) method, this paper proposes a new method, SQG‐mEOF‐R, to estimate the interior density from the sea surface density and sea surface...

As in-situ observations are sparse, targeted observations of a specific mesoscale eddy are rare. Therefore, it is difficult to study the three-dimensional structure of moving mesoscale eddies. From April to September 2014, an anticyclonic eddy located at 135°E–155°E, 26°N–42°N was observed using 17 rapid-sampling Argo floats, and the spatiotemporal...

The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as groun...

Risk assessment and emergency responses to ensure the safety of ships crossing the Arctic have gained tremendous attention in recent years. However, asymmetry in the probability that people will receive aid when navigating through the Arctic still exists because of the unsystematic allocation of rescue bases in the Arctic. At the same time, no stud...

Offshore wind energy has become the fastest growing form of renewable energy for the last few years. And the development of offshore wind farms (OWFs) is now characterized by a boom. OWF siting is crucial in the success of wind energy projects. Therefore, this paper aims to introduce intelligent algorithms to improve the siting assessment under con...

With the accelerated warming of the arctic and the gradual opening of the Arctic passages, more and more attention has been paid to assessing the risk of the navigation environment in the Arctic. Due to the scarcity of visibility data in the Arctic, this study proposes a model for referring visibility based on a back propagation (BP) neural network...

The ordered clustering problem in the context of multicriteria decision aid has been increasingly examined in management science and operational research during the past few years. However, the existing clustering algorithms may not provide an exact suggestion for a partition number for decision makers by using the diagram method. In addition, thes...

In this study, subsurface eddies near the Vietnam coast of the South China Sea were observed with in situ observations, including Argo, CTD, XBT and some processed and quality controlled data. Based on temperature profiles from four Argo floats near the coast of Vietnam, a subsurface warm eddy was identified in spring and summer. The multi-year Arg...

Global sea surface salinity (SSS) has been obtained from space since 2009 by the Soil Moisture and Ocean Salinity (SMOS) mission and has been further enhanced by Aquarius in 2011 and Soil Moisture Active‐Passive (SMAP) missions in 2015. Due to the differences between SMOS, Aquarius, and SMAP in the instruments used, retrieval algorithms, and error...

This paper provides a new model for simulation of the Northern Sea Route (NSR) navigation environment and maritime trade where the game theory and hesitant fuzzy set theory are incorporated to tackle the interaction of different sea routes and uncertainties exiting in multi-climate prediction. The proposed model contains navigable assessment, route...

This paper presents a new route planning system for the purpose of evaluating the strategic prospects for future Arctic routes. The route planning problem can be regarded as a multi criteria decision making problem with large uncertainties originating from multi-climate models and experts’ knowledge and can be solved by a modified A* algorithm wher...

A non-linear, empirical method, called generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), is proposed to estimate subsurface salinity profiles from sea surface parameters in the Pacific Ocean. The purpose is to evaluate the ability of the FOAGRNN methodology and satellite salinity data to reconstruct salinity...

This paper proposes a generalised Nash equilibrium model to investigate the interaction between the Arctic routes and current routes with incomplete information on Arctic ice condition, which leads to the Arctic or non-Arctic company profits and container shipping allocation change and to examine if Arctic routes can be used as a "relief valve" for...

This paper proposes a bi-level model from the perspective of game theory to describe the effect of the rise of Arctic shipping routes on traditional routes and the response of the traditional routes. The upper-level model demonstrates the competition between shipping companies that maximise their own profits via speed adjustment, which can be prese...

An improved spatial-temporal interpolation method, which can eliminate suspicious interpolated values effectively and consider both direction anisotropy and angle-based weight, is proposed to overcome the insufficiency of the traditional spatial-temporal interpolation. By using the traditional and improved spatial-temporal interpolation respectivel...

Experience has shown that researchers and engineers are unable to construct ideal models for risk assessment and make optimal decisions in situations with insufficient data. A nonlinear risk assessment model is therefore proposed in this study based on an improved projection pursuit model (IPPM) for use in situations where insufficient data are ava...

An improved scoring search algorithm based on information flow is proposed for Bayesian network structure learning. Firstly, the 0/1 optimization problem is constructed based on the information flow for global causal analysis, and the optimal initial network structure is obtained. Then, the search space is generated based on the initial structure,...

The application of Bayesian network (BN) theory in risk assessment is an emerging trend. But in cases where data are incomplete and variables are mutually related, its application is restricted. To overcome these problems, an improved BN assessment model with parameter retrieval and decorrelation ability is proposed. First, multivariate nonlinear p...

With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstr...

In drought years, it is important to have an estimate or prediction of the probability that a water shortage risk will occur to enable risk mitigation. This study developed an improved logistic probability prediction model for water shortage risk in situations when there is insufficient data. First, information flow was applied to select water shor...

How to extract the causal relations in climate-cyclone interactions is an important problem in atmospheric science. Traditionally, the most commonly used research methodology in this field is time-delayed correlation analysis. This may be not appropriate, since a correlation cannot imply causality, as it lacks the needed asymmetry or directedness b...

With the objective of tackling the problem of inaccurate long-term El Niño Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamic reconstruction...

Despite much previous effort, the establishment of an accurate model of the western Pacific subtropical high (WPSH) and analysis of its chaotic behavior has proved to be difficult. Based on a phase-space technique, a nonlinear dynamical model of the WPSH ridge line and summer monsoon factors is constructed here from 50 years of data. Using a geneti...

Several remotely sensed sea surface salinity (SSS) retrievals with various resolutions from the soil moisture and ocean salinity (SMOS) and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles (S) using multilinear regressions. The performance is evaluated using a total root mean square (RMS) error, different error sources...

Prediction in Ungauged Basins (PUB) is an important task for water resources planning and management and remains a fundamental challenge for the hydrological community. In recent years, geostatistical methods have proven valuable for estimating hydrological variables in ungauged catchments. However, four major problems restrict the development of g...

Determining OWA (ordered weighted averaging) weights has received more and more attention since the appearance of the OWA operator. Based on the principle of least mean squared errors, a new parametric OWA operator is proposed to obtain its associated weights. In coordination with fuzzy inference and a few of judgments on weights provided by decisi...

The intuitionistic fuzzy decision making problems have gained great popularity recently. Most of the current methods depend on various aggregation operators that provide collective intuitionistic fuzzy values of alternatives to be ranked. Such collective information only depicts the overall characteristics of the alternatives but ignores the detail...

In terms of drought years, the assessment of water shortage risk is a significant precondition for taking effective measures to reduce the potential losses. This paper proposes a new multiple integral model for evaluating the risk of water shortage. First, the probability density function for water shortage was simulated. Second, a nonlinear functi...

The evaluation of human environment risk is lacking quantitative data, while the qualitative knowledge cannot be easily quantified and synthesized. Furthermore, sometimes the experts are not well acknowledged with the whole indicator system or cannot reach an agreement on the comments. The conventional evaluation methods are not competent to solve...

With the launch of the Soil Moisture and Ocean Salinity (SMOS) and American Aquarius, different sea surface salinity (SSS) gridded products have been developed by several institutions, including SMOS Locean L3 data released by the Centre Aval de Traitement des Données SMOS (CATDS) in France, SMOS BEC L4 data released by the Barcelona Expert Centre...

In traditional analytic hierarch process (AHP), decision makers (DMs) are required to provide crisp judgments over paired comparisons of objectives to construct comparison matrices. To enhance the modeling ability of traditional AHP, we propose hesitant AHP (H-AHP) that can consider the hesitancy experienced by the DMs in decision. H-AHP is charact...

Based on the adaptive network fuzzy inference system (ANFIS), methods to filter out the noise of impact factors from the main signal are discussed. Focusing on the abnormal weather conditions in 2010, we use the delay-relevant method to analyze the five members of the summer monsoon system that had the largest effect on the subtropical high anomali...

Scholars have been showing great interest in revealing the mechanisms that govern the activities of the western Pacific subtropical high (WPSH). However, the problem currently remains unresolved. In this paper, a new model is developed to reveal the dynamical mechanism of the WPSH abnormal activities. Variables in the partial differential vorticity...

A new dynamical-statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization functio...

The analytic network process (ANP) is a methodology for multi-criteria decision making used to derive priorities of the compared elements in a network hierarchy, where the dependences and feedback within and between the elements can be considered. However, the ANP is limited to the input preferences as crisp judgments, which is often unfavorable in...

In this paper, to naturally fill the gap in incomplete data, a new algorithm is proposed for estimating the risk of natural disasters based on the information diffusion theory and the equation of the vibrating string. Two experiments are performed with small samples to investigate its effectiveness. Furthermore, to demonstrate the practicality of t...

To address the inaccuracy of long-term El Niño-Southern Oscillation (ENSO) forecasts, a new dynamical-statistical forecasting model of the ENSO index was developed based on dynamical model reconstruction and improved self-memorization. To overcome the problem of single initial prediction values, the largest Lyapunov exponent was introduced to impro...

Abnormal Western Pacific subtropical high (WPSH) activities often lead to extreme weather events in East Asia in some years. The relationship between the WPSH and the members of the East Asian summer monsoon (EASM) system is unknown, however. So the forecasting of abnormal WPSH activities is still difficult. Because of adaptive learning and nonline...