中国东南沿海重点城市台风危险性分析

中国东南沿海地区是中国经济最发达，人口最稠密的地区，同时也是世界上受台风影响最严重的地区之一。本文以上海经9个沿海重点为例，通过对《台风年鉴》数据的分析处理，提取了台风关键参数并进行了统计分析与建模；利用Monte Carlo抽样和成熟台风风场模型模拟，得到了极值风速序列，对年极值风速序列进行极值风速统计分析，得到了不同重现期和地貌的极值风速，结果与实测风速比较吻合。     

A comparative study of risk perception in small and large communities where chemical industries are present

Opinion polls were conducted to assess the level of risk perceived by the citizens in communities of different sizes where chemical industries were present. The aim of the study was to relate the risk perceived to variables such as the nature of risks, the specific information received regarding the industrial activity and the economic impact of this activity in the community.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

THE DEVELOPMENT OF A MODEL TO EVALUATE HYDROLOGIC RISK IN WATER RESOURCE SYSTEM DESIGN1

In this paper, a procedure for analyzing a water resource system with special emphasis on evaluation of acceptable economic risk due to occasional failures to deliver water is proposed. The basic methodology includes the development of a simple mathematical model which describes the physical hydrologic and economic characteristics of a single reservoir irrigation and city water supply system and an evaluation of economic benefits of the system with full and partial deliveries of water. The system is simulated for various combinations of decision variables (system magnitudes) and an optimum design is obtained by response surface technology. Emphasis is placed on the basic model and methodology although, in order to introduce some realism, the procedure is applied to data based on the existing reservoir system on the South Concho River in West Central Texas.     

The impact of global warming on winter tourism and skiing: a regionalised model for Austrian snow conditions

Possible climate change will modify snow-cover depth and change the characteristics of winter tourism and skiing districts. Our model describes seasonal snow-cover depth related to altitude in six Alpine climate regions as the best fit of all snow stations. Data cover 30 winter seasons (November to April values) from 1965 to 1995. We modified the data according to a scenario of temperature and precipitation change (2 °C warming, no precipitation change) and achieve a new simulated snow-cover depth. The indicators MARP (mean altitude of resident population) and MASPSL (mean altitude of starting point of ski lifts) serve as references for “critical altitudes” of Austrian districts. A warming implies a reduction of snow in all districts, but the loss is overproportional in lower altitudes. The direction of economic impacts is clear – income losses and adaptation costs – but magnitude and time frames remain uncertain. Received: 24 February 1999 · Accepted: 15 May 1999     

Constructing and justifying risk and accountability after extreme events: public administration and stakeholders’ responses to a wildfire disaster

ABSTRACT

The impacts of extreme weather events, causing severe storms and wildfires, cascade across administrative borders within a country, challenging the steering capacity of governance networks at different political scales. This paper examines how accountability and risk were constructed and negotiated in the aftermath of Sweden’s largest wildfire. It draws on results from an interview study with executives of organizations and landowners involved, and an analysis of government reports about the wildfire’s cause and consequences. Although the fire was human-caused, public administrative bodies paid considerable attention to the local emergency services and their poor handling of the wildfire, caused by lack of knowledge of forest fire behavior. The study confirms many of the challenges associated with governance networks. It finds that issues about who to hold accountable, in what forum and for what issue are not fully addressed, being overwhelmed by demands for better knowledge of forest fire prevention and improved coordination and collaboration. To conclude, the paper calls for a better-informed public administration, forest sector and interrelated networks that take responsibility for their actions or lack thereof.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

The ecological effects of trichloroacetic acid in the environment

Trichloroacetic acid (TCAA) is a member of the family of compounds known as chloroacetic acids, which includes mono-, di- and trichloroacetic acid. The significant property these compounds share is that they are all phytotoxic. TCAA once was widely used as a potent herbicide. However, long after TCAA's use as a herbicide was discontinued, its presence is still detected in the environment in various compartments. Methods for quantifying TCAA in aqueous and solid samples are summarized. Concentrations in various environmental compartments are presented, with a discussion of the possible formation of TCAA through natural processes. Concentrations of TCAA found to be toxic to aquatic and terrestrial organisms in laboratory and field studies were compiled and used to estimate risk quotients for soil and surface waters. TCAA levels in most water bodies not directly affected by point sources appear to be well below toxicity levels for the most sensitive aquatic organisms. Given the phytotoxicity of TCAA, aquatic plants and phytoplankton would be the aquatic species to monitor for potential effects. Given the concentrations of TCAA measured in various soils, there appears to be a risk to terrestrial organisms. Soil uptake of TCAA by plants has been shown to be rapid. Also, combined uptake of TCAA from soil and directly from the atmosphere has been shown. Therefore, risk quotients derived from soil exposure may underestimate the risk TCAA poses to plants. Moreover, TCE and TCA have been shown to be taken up by plants and converted to TCAA, thus leading to an additional exposure route. Mono- and di-chloroacetic acids can co-occur with TCAA in the atmosphere and soil and are more phytotoxic than TCAA. The cumulative effects of TCAA and compounds with similar toxic effects found in air and soil must be considered in subsequent terrestrial ecosystem risk assessments.