Fushen Zhang

Ontology-based representation of meteorological disaster system and its application in emergency management: Illustration with a simulation case study of comprehensive risk assessment

Purpose – The purpose of this paper is to make research on causing mechanism of meteorological disaster as well as the components of meteorological disaster system and their semantic relationships. It has important practical significance due to the urgent need of further providing support for pre-assessment of influences of disastrous weather/climate events and promoting the level of emergency management. Design/methodology/approach – This paper analyses the occurrence regulations and components of meteorological disasters and proposes the concept of meta-action. Ontology modelling method is adopted to describe the components and relationships among different parts comprising meteorological disaster system, and semantic web rule language is selected to identify the implicit relationships among the domain knowledge explicitly defined in ontology model. Besides, a case is studied to elaborate how to provide logic and semantic information support for comprehensive risk assessment of disastrous weather/climate events based on rule-based ontology reasoning method. It proves that ontology modelling and reasoning method is effective in providing decision makings. Findings – This paper provides deep analyses about causing mechanisms of meteorological disasters, and implements information fusion of the components of meteorological disaster system and acquisition of potential semantic relations among ontology components and their individuals. Originality/value – In this paper, on the basis of analysing the disaster-causing mechanisms, the meteorological disaster ontology (MDO) model is proposed by using the ontology modelling and reasoning method. MDO can be applied to provide decision makings for meteorological departments.

Spatial Estimation of Losses Attributable to Meteorological Disasters in a Specific Area (105.0°E–115.0°E, 25°N–35°N) Using Bayesian Maximum Entropy and Partial Least Squares Regression

The spatial mapping of losses attributable to such disasters is now well established as a means of describing the spatial patterns of disaster risk, and it has been shown to be suitable for many types of major meteorological disasters. However, few studies have been carried out by developing a regression model to estimate the effects of the spatial distribution of meteorological factors on losses associated with meteorological disasters. In this study, the proposed approach is capable of the following: (a) estimating the spatial distributions of seven meteorological factors using Bayesian maximum entropy, (b) identifying the four mapping methods used in this research with the best performance based on the cross validation, and (c) establishing a fitted model between the PLS components and disaster losses information using partial least squares regression within a specific research area. The results showed the following: (a) best mapping results were produced by multivariate Bayesian maximum entropy with probabilistic soft data; (b) the regression model using three PLS components, extracted from seven meteorological factors by PLS method, was the most predictive by means of PRESS/SS test; (c) northern Hunan Province sustains the most damage, and southeastern Gansu Province and western Guizhou Province sustained the least.

Exploring Mean Annual Precipitation Values (2003–2012) in a Specific Area (36°N–43°N, 113°E–120°E) Using Meteorological, Elevational, and the Nearest Distance to Coastline Variables

Gathering very accurate spatially explicit data related to the distribution of mean annual precipitation is required when laying the groundwork for the prevention and mitigation of water-related disasters. In this study, four Bayesian maximum entropy (BME) models were compared to estimate the spatial distribution of mean annual precipitation of the selected areas. Meteorological data from 48 meteorological stations were used, and spatial correlations between three meteorological factors and two topological factors were analyzed to improve the mapping results including annual precipitation, average temperature, average water vapor pressure, elevation, and distance to coastline. Some missing annual precipitation data were estimated based on their historical probability distribution and were assimilated as soft data in the BME method. Based on this, the univariate BME, multivariate BME, univariate BME with soft data, and multivariate BME with soft data analysis methods were compared. The estimation accuracy was assessed by cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results showed that multivariate BME with soft data outperformed the other methods, indicating that adding the spatial correlations between multivariate factors and soft data can help improve the estimation performance.

Precipitation Interpolation by Multivariate Bayesian Maximum Entropy Based on Meteorological Data in Yun- Gui-Guang region, Mainland China

Precipitation interpolation has been a hot area of research for many years. It had close relation to meteorological factors. In this paper, precipitation from 91 meteorological stations located in and around Yunnan, Guizhou and Guangxi Zhuang provinces (or autonomous region), Mainland China was taken into consideration for spatial interpolation. Multivariate Bayesian maximum entropy (BME) method with auxiliary variables, including mean relative humidity, water vapour pressure, mean temperature, mean wind speed and terrain elevation, was used to get more accurate regional distribution of annual precipitation. The means, standard deviations, skewness and kurtosis of meteorological factors were calculated. Variogram and cross- variogram were fitted between precipitation and auxiliary variables. The results showed that the multivariate BME method was precise with hard and soft data, probability density function. Annual mean precipitation was positively correlated with mean relative humidity, mean water vapour pressure, mean temperature and mean wind speed, negatively correlated with terrain elevation. The results are supposed to provide substantial reference for research of drought and waterlog in the region.

Spatial Estimation of Mean Annual Precipitation (1951–2012) in Mainland China Based on Collaborative Kriging Interpolation

Spatially explicit distribution of mean annual precipitation are required in the quantitative research on several water-related issues. The difference of distribution of precipitation has complicated reasons, one of them being the spatial correlation between multivariate meteorological factors. In this study, collaborative kriging interpolation (CKI) was used to estimate the spatial distribution of mean annual precipitation in China. Precipitation data from 756 meteorological stations were used, and spatial correlations between seven meteorological factors were analyzed, including annual precipitation, average barometric pressure, average wind speed, average temperature, average water pressure, average relative humidity, and annual average sunshine hours. The estimation results were assessed by means of cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that adding the spatial correlation analysis between multivariate meteorological factors can help improve the prediction performance.

Emergency Case Retrieval Based on Fuzzy Sets and Text Mining

Emergency management is such a domain where experiential knowledge could be easily collected, and is quite suitable for the application of case based reasoning. However, in practice there are two problems limiting the effectiveness of case based reasoning (CBR), the unstructured information and changing situation. This paper proposed an approach based on fuzzy sets and text mining to solve those two problems, which contains three steps: (a) representing the case based on fuzzy sets and text mining, (b) retrieving the similar case based on text classification, and (c) establishing connections of attributes and solutions based on Hownet. This approach could not only retrieve the most similar case, but also extract specific solutions from several cases.

Study on Scene-Driven Emergency Drill Method

Based on the analysis of traditional emergency drills and simulated emergency drill characteristics, the construction and express method of structured scene model was proposed, including the emergency scene classification, the setting of evolution and development conditions and the structured expression of emergency scene. The scene-driven key technology and model was described, such as the evolutionary relationship of scenes, the reasoning model of scene chain and transition model of scene. On the basis, the emergency drill model of scene-driven was proposed. The model is dynamic and has high similarity with the real emergencies, which plays an important role to improve the effect of emergency drills.

Pre-evaluation of Contingency Plans for Meteorological Disasters Based on LINMAP Method

As the guidelines of dealing with meteorological disasters, whether contingency plans are perfect or not will affect the efficiency of the emergency response directly. In this paper, the process of pre-evaluation of contingency plans is expounded in detail: Firstly, this process is based on multi-attribute decision-making (MADM) method, which is to find a best solution from all feasible plans assessed on multiple attributes; secondly, the linear programming technique for multi-dimensional analysis of preference (LINMAP) method is used to develop decision analysis and generates the best alternative as the solution; thirdly, the typical evaluation indexes are selected and a numerical example is presented to demonstrate the validity and applicability of the proposed method. Finally, because the pre-evaluation results are difficult to verify, Copeland rule is used to colligate the ranking orders of different decision departments.

A Preliminary Research on Incident Chain Modeling and Analysis

In the real world, an incident is often followed by some secondary or derivative incidents which may further trigger some new incidents. The process goes so repeatedly. How to present this kind of chain relationship and formalize cascading effect of these incidents is very important for prevention and control of the complicated incident scenarios like this. This paper preliminarily explores incident chain modeling including the mechanism of incident chain and its construction and presentation. In mechanism, a term meta-force is coined for theoretical research of the incident chain modeling. A general theoretical framework including incident, meta-force and disaster receptor is proposed which can be used for incident chain research. Following the research on construction and presentation of incident chain modeling, the paper further investigates the analysis approach to incident chain based on Bayesian network. As a kind of natural presentation of incident chain, Bayesian network techniques have inherent advantages when used in inference and prediction of a complicated disaster scenario. Concrete applications include risk analysis and decision optimization. The work can be consulted in case of prevention and control of complicated disaster circumstances.