Measuring Social, Health and Economic Impacts of Disasters: Experiences from 9 MICRODIS sites, Vietnam

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Measuring Social, Health and Economic Impacts of Disasters: Experiences from 9 MICRODIS sites EXPERIENCES FROM VIETNAM Tran Huu Tuan; Hue University, Vietnam [email_address] 3 rd IDRC CONFERENCE 30 MAY - 3 JUNE 2010; DAVOS, SWITZERLAND

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Figure 2: Types of injuries due to floods 2007 Health impacts (cont.) ,[object Object]

Figure 3: Reasons to injuries between flooded & less-flooded villages Health impacts (cont.) ,[object Object]

Figure 4: 2007 floods and local food security Social impacts ,[object Object]

Figure 5: Sources of support for local communities to recover from flood impacts Social impacts (cont.) ,[object Object]

Assessing physical damages to floods The poor have significantly less absolute damage but they are more vulnerable. Examining the relationship between flood damage, flood damage as share of household income and income distribution Economic impacts 0.000 14.06 (19.73) 27.62 (26.48) Average flood damage as share of household income; % (std. dev.) 0.093 5,090,586 (9,007,653) 3,689,750 (5,223,597) Household damage due to 2007 floods; mean (VND); (std. dev.) Sig. Above poverty line Below Poverty line Types of huseholds

[object Object],[object Object],Investigating the relationship between flood damage and household’s income dependence on natural resources Economic impacts (cont.) 0.007 3,320,856 (6,144,137) 5,050,280 (9,608,627) Household damage due to 2007 floods; mean (VND);(sd) Sig. Not fully NR dependence income Fully NR dependence income Household’s income dependence on NRs

[object Object],[object Object],Exploring the relationhip between flood damage and flood risk exposure Economic impacts (cont.) 0.184 3,178,494 (7,030,011) 4,044,172 (7,825,832) Household damage due to 2007 floods; mean (VND); (std. dev.) Sig. Less-flooded villages Flooded villages Type of villages

Relationship btw acted upon early warning message and flood damage Economic impacts (cont.) ,[object Object],[object Object],F = 1,565; Sig. = 0,212 4.389.551 Total 1.606.923 No, did not act upon early warning message 4.469.055 Yes, acted upon early warning message Total damage due to floods 2007 (VND) Did you act upon early warning message?

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[object Object],[object Object],[object Object],[object Object],Determinants of WTC -1408.7968 Log likelihood 0.0013 Prob > chi2 .7252901*** 0.74 If household located in flooded villages (flooded =1, less flooded =0) Flood exposured .0084333 3.82 Total damage due to flood (million VND) Flood damage .6776564*** 2.72 Availability of labour (labours) Labour availability .5714322 0.96 If households acted upon warming message (yes = 1, 0 otherwise) Flood protection .4118738** 0.74 No. of floods that household experienced (no. of floods) Floode experience .0606846*** 19.14 Total household income per year (million VND) Income .1357486 2.98 Level of education (from 1= not completed any school to 8 = graduated from university) Education -.0261874 51.34 Age of respondent (years) Age -.3732771 0.75 Gender of respondent (male = 1; female = 0) Gender 3.792849* - Constant 7.68 Willingess to contribute (WTC) labour (person-days) WTC Tobit regression Mean values Description of variables Variables

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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1. Measuring Social, Health and Economic Impacts of Disasters: Experiences from 9 MICRODIS sites EXPERIENCES FROM VIETNAM Tran Huu Tuan; Hue University, Vietnam [email_address] 3 rd IDRC CONFERENCE 30 MAY - 3 JUNE 2010; DAVOS, SWITZERLAND

8. Assessing physical damages to floods The poor have significantly less absolute damage but they are more vulnerable. Examining the relationship between flood damage, flood damage as share of household income and income distribution Economic impacts 0.000 14.06 (19.73) 27.62 (26.48) Average flood damage as share of household income; % (std. dev.) 0.093 5,090,586 (9,007,653) 3,689,750 (5,223,597) Household damage due to 2007 floods; mean (VND); (std. dev.) Sig. Above poverty line Below Poverty line Types of huseholds

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