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            we quantitatively investigating the past trend of natural disasters, focusing upon earthquakes and tsunamis, which occurred in Japan and Indonesia with respect to their occurrences and human casualties; including both deaths and missing people (D&M). We apply mathematical policy analysis techniques in our natural disaster risk analysis and assessment in order to develop policies to mitigate the casualties caused by these natural disasters. First, we review the historical trend of earthquakes and tsunamis related to their occurrences and D&M from 1900 to 2012 to explain their occurrence frequency and forecast the D&M using probabilistic models. We divide the entire period into three time-periods and compare their tendency in both countries. Using about 100 years of data, our study confirms that the Exponential distribution fits the data of inter-occurrence times between two consecutive earthquakes and tsunamis, while the Poisson distribution fits the data of D&M. The average numbers of inter-occurrence times of earthquakes for Japan and Indonesia are 186.23 days and 167.77 days, respectively, whilst those of tsunamis are 273.31 days and 490.71 days, respectively. We find that earthquakes with magnitudes ranging from 6.0Mw to 7.4Mw and having epicenters in the mainland cause more casualties, while those with magnitudes 7.5Mw and above and having epicenters offshore/at sea cause relatively fewer casualties. This implies that mainland earthquakes have higher probability to bring more casualties than the sea earthquakes. In terms of fatalities, earthquakes and tsunamis have caused more deaths in Japan than in Indonesia .

                As a continuation   which is included in the activities carried out during the first phase of disaster management, the timing and magnitude of natural disasters are unpredictable, and thus are stochastic. Number of death and missing people (D&M) caused by natural disasters are often used to measure the magnitude of the disasters. By using statistical analysis, we investigate the relationship between the D&M inflicted and some parameters of natural disasters with case studies of earthquakes and tsunamis occurred in Japan and Indonesia from 1900 to 2012. The parameters under investigation are the epicenter location, earthquake magnitude, depth of hypocenter, and water height. We found that the earthquake magnitude and water height are positively affect the D&M inflicted, while the epicenter location and hypocenter depth have significant and negative effect. In addition, in Chapter III we also review the recovery process from the 2004 Aceh tsunami and the 2011 Tohoku tsunami, especially in the agriculture sector.

             we measure the damaging impacts due to the 2011 GEJE that hit Japan on March 11, 2011 and discuss about the recovery process, especially on the agricultural and manufacturing sectors. Three years have passed since the 2011 GEJE hit the northeastern part of Japan. The earthquake then triggered a devastating tsunami and a nuclear accident, which in turn created a compound disaster that claimed a large number of human casualties and devastated properties. The 2011 GEJE caused the economy growth to decline by 2.2% with the largest decrease experienced by the industrial sector (-7.1%), followed by the agricultural sector (-3.6%) and the services sector (-0.2%). The agriculture and manufacturing sectors underwent large decreases in growth since the economies of most of the affected prefectures have relied on these two sectors. Thus, by investigating the damaging impacts of the 2011 GEJE we try to evaluate the restoration and reconstruction performance in the agriculture and manufacturing sectors. Our study finds that there has been significant progress made towards restoration and reconstruction on the areas affected by the disaster. Using prefectural data from 2000 to 2012, we apply econometric methods based upon the bias-corrected least-squares dummy variable to estimate the impact of the 2011 GEJE on the agricultural and manufacturing sectors. From this analysis, two major insights emerged. First, the 2011 GEJE had a significant negative impact on agriculture and manufacturing sectors. On average, the impact on the agriculture sector was higher than on the manufacturing sector, specially, about twice as large. Second, in each sector, the impact of the disaster was perceived differently depending on the region. In both the agriculture and manufacturing sectors, the most affected prefectures experienced about triple the impact that the less affected prefectures underwent.

                although it cannot be denied, that there are still many people's lives greatly inconvenienced because of the damage caused, mainly in the disaster-hit areas and elsewhere in the country, but there has been significant progress made towards restoration and reconstruction on the areas affected by the disaster in the two years since. One of the important lessons learned from the recovery process due to the 2011 GEJE is that nimble handling and comprehensiveness as well as good cooperation from all parties are the keys to success in the recovery process after any major disaster, in which according to MOFA, Japan has received, so far, assistance from 163 countries and 43 international organizations.
Given a seriously emergent situation occurring e.g. just after large-scale natural disasters and so on, how to deal with victims, survivors, and damaged areas is a very critical and important problem. There are short-term and long-term responding strategies to be taken by the public sector. The former includes how to distribute necessary goods to the damaged area and transport them corresponding to their supply and demand situation as quickly as possible while the latter corresponds to trying to make long-term future plan for e.g. building new infrastructures and then making city planning. In order to obtain an optimal strategy for the former problem we try to make necessary and desirable response strategies for managing emergent cases caused by various natural disasters by solving multi commodity transshipment network flow optimization problems under various types of uncertain situations ...........

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