Because the government changed laws and opened the market progressively in recent years, the financial market in Taiwan becomes more and more liberal and international; every investor has to face a more complicated investitive environment. They can choice many investitive objects and tools, but how to choice the best one is a big problem for them and the risk in the financial market becomes much higher. Mutual fund is a popular investment tools in recent year. One of the mutual fund¡¦s benefits is the diversity of investment and effectively disperses risk.. In August 2006, the government in Taiwan opens up the market of mutual fund; the investors can buy offshore mutual funds in many channels, so they can choice many kinds of mutual funds, about 1,400 in April 2008. Also, every investor that can beat the level of risk is so different, it maybe make them confused and really want to know which one is much better and do asset allocation very well. Therefore, how to design a good portfolio for different perceived risk levels of investors is a worthful topic in the academia and the really world. This research uses the genetic algorithm to construct mutual fund portfolios based on perceived risk levels, use fund return, standard deviation, Alpha, Beta, Sharpe, IR and Sortino indicators to select funds of a portfolio and calculate portfolio return and standard deviation, then do asset allocation. This research change funds in every portfolio every month using Sliding Windows method from Jan 1, 2001 to Dec 12, 2007, totally 84 times. The result of this research is every portfolio average return wins benchmark index average return. The standard deviation of every portfolio also wins benchmark index standard deviation. It shows this research can beat benchmark index effectively and also can decrease the risk of portfolio return, then we can get a good fund portfolio for different perceived risk levels of investors

Weather conditions have certain impacts on roadway traffic operations, especially traffic safety. Bridges differ from most surface streets and highways in terms of their physical properties and operational characteristics. This research assess the driving risk under different weather conditions through focus group firstly, then it develops a multi-ordered discrete choice model that is used to analyze and evaluate driving risks under both single and dual weather conditions. The data is derived from an extensive questionnaire survey in Shanghai. And the questionnaire includes those factors related to roadway, drivers, vehicles, and traffic that may have significant impacts on traffic safety under severe weather conditions. Considering the actual situation these variables except driver's gender are selected as independent variables of risk evaluation. As a result, different risk levels and corresponding probability are calculated, which are very important to optimize emergency resource allocation and make reasonable emergency measures. Moreover, in order to reduce severe bridge-related crashes, the research develops an ordered probit model to analyze those factors contributing to bridge-related crash severity and to predict probabilities of different severity levels under rainy conditions.

Regional landslide risk, as it is most commonly defined, is a product of the following: hazard, vulnerability and exposed population. The first objective of this research project is to estimate the regional landslide hazard level by calculating its probability of slope failure based on maximum slope angles, as estimated using data provided by digital elevation models (DEM). Furthermore, it addresses the impact of DEM resolution on perceived slope angles, using local averaging theory, by comparing the results predicted from DEM datasets of differing resolutions. Although the likelihood that a landslide will occur can be predicted with a hazard assessment model, the extent of the damage inflicted upon a region is a function of vulnerability. This introduces the second objective of this research project: vulnerability assessment. The third and final objective concerns the impact of urbanization and population growth on landslide risk levels.