Asset exposure of Asian cities to natural hazards metro manila

• Main Author: Shi, Linus Ling Yan
• Other Authors: Lo Yat-Man, Edmond
• Format: Final Year Project
• Language: English
• Published: 2016
• Subjects: DRNTU::Engineering
• Online Access: http://hdl.handle.net/10356/67917
• Institution: Nanyang Technological University

Natural disasters affect thousands of lives every year. Modern technology is not able to eliminate the negative impact brought by disastrous events such as tsunamis, earthquakes and typhoon. The timing and scale of natural disasters are nearly impossible to be known before it happens, however, it is possible to assess the assets exposed to such natural disasters. It is important to have a good prediction of the asset exposure, as it enables cities and countries to be better prepare in handling the loss from and recovering after the disasters. The Philippines, located along the Ring of Fire - a large Pacific Ocean region where many of Earth's volcanic eruptions and earthquakes occur, has been greatly impacted by an inexhaustible number of deadly natural disasters such as typhoons, earthquakes, volcanic eruptions. Metro Manila, as the National Capital Region of Philippines, plays an important role in the economic performance of the country. Thus it is an ideal city to carry out this research, as the result can be possibly used to mitigate the negative impact brought by the national disaster to the Philippines. Values of transportation infrastructures are being used as indicators in this project. Two models, namely Power-Law Scaling Equation and Log-Log Regression are used to analyze the projection of the cumulative construction value of roads and bridges, which are the most common transportation infrastructures. An estimation of population, based on Philippines statistic yearbook, is also used in the Power Law Scaling Equation, to get a prediction of cumulative road and bridge value from 2010 to 2050. In contrast, the Log-Log Regression model computes cumulative value of road construction by using population density, income per capita and gross regional domestic product as the independent variables.