A New Marine Disaster Assessment Model Combining Bayesian Network with Information Diffusion

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 information diffusion algorithm is adopted to expand associated samples between disaster losses and environmental conditions. Then the expanded data sets are used to build the BN-based assessment model through structural learning, parameter learning and probabilistic reasoning. The proposed model is applied to the hazard assessment of marine disasters in Shanghai. Experimental comparison results show that it is capable of dealing with uncertainty effectively and achieving more accuracy risk assessment under the small sample condition.

Trends in social vulnerability to storm surges in Shenzhen, China

An evaluation of social vulnerability to storm surges is important for any coastal city to provide marine disaster preparedness and mitigation procedures and to formulate post-disaster emergency plans for coastal communities. This study establishes an integrated evaluation system of social vulnerability by blending a variety of single-evaluation methods, which are subsequently combined by weighting in order to calculate a common social vulnerability index. Shenzhen has a current reputation of having considerable economic development potential and is a representative city in China. It is chosen for an evaluation of its social vulnerability to storm surges via a historical social and economic statistical dataset spanning the period 1986–2016. Exposure and sensitivity increased slowly with some fluctuation, leading to some alterations of the social vulnerability trend. Social vulnerability stayed almost constant during 1986–1991 and 1993–2004, while it decreased sharply afterwards to form a “stair-type” declining curve over the past 31 years. Resilience is progressively increasing by virtue of a continuous increase in medical services supply, fixed asset investments, and salary levels of employees. These determinants contribute to the overall downward trend of social vulnerability for Shenzhen.

Intercomparison of Assimilated Coastal Wave Data in the Northwestern Pacific Area

The assimilated coastal wave data are useful for wave climate study, coastal engineering, and design for marine disaster protection. However, the assimilated coastal wave data are few. Here, wave analysis data produced by the JMA (Japan Meteorological Agency) and ERA5 wave data were compared with GPS (Global Positioning System) buoy-measured wave data. In addition, the accuracy of ERA5 wave data for various conditions was investigated. The accuracy of JMA analysis wave height was better than that of ERA5 wave height. The ERA5 wave height was underestimated as the wave height increased. The accuracy of the ERA5 wave height was significantly different in fetch-unlimited and fetch-limited conditions. The difference of the skill metrics between fetch-unlimited and fetch-limited conditions was due to the overestimation of the fetch in the ERA5 grid. This result also applied to the wave period.

A Comprehensive Method for Evaluating Marine Disaster Risk Reduction Capacity in China

A region’s capacity for marine disaster risk reduction is characterized by the resources that can be mobilized. These resources include pre-disaster defense, disaster monitoring, warning, emergency response, post-disaster restoration, and reconstruction. It is a very important index to effectively evaluate the regional capacity to overcome marine disasters. At present, there is no unified model and method for comprehensively evaluating the regional marine disaster reduction capacity. This study proposes a novel evaluation index system for a county-level administrative region using expert opinions, questionnaires, and analytic hierarchy process methods. Based on the comprehensive evaluation in three pilot areas, the current situation of regional marine disaster reduction capacity is analyzed, which would contribute to the effective management of marine disaster risks in the future. The results and experiences are of great value to future disaster reduction capacity assessment promotion and practice in all coastal counties of China.

Coastal Marine Automated Observing Station in China

The coastal marine automated observing station (CMAOS) is a facility built on the coast or island, collecting time series of ocean environment variables such as marine hydrological and meteorological parameters. This article introduces the general progress of CMAOS in China. The greatest demand for CMAOS is marine disaster forecasting and early warning to assist decision making and to protect the marine ecological environment. After more than half a century's efforts, China has established a network of CMAOS covering the coastal areas of China, which in real time acquires marine environmental information. The core of CMAOS is the marine automated observing system (MAOS), which integrates ocean sensors, a data collector, and a data reception processor. Currently, the MAOS is in its third generation of development and will follow the trend of unmanned, intelligent, and multifunctional systems. With the continuous implementation of relevant planning of an ocean observing network in China, the layout of CMAOS will be further optimized, the observed parameters will be more comprehensive, and the measuring means will be more abundant.