However, technology can deliver crucial information such as the extent of risk due to natural calamities in a given location, or the estimated cost for a city to recover from a catastrophe. Governments and industries can then assess risk scientifically and plan around it. If governments are able to come up with an estimate to losses within hours of a calamity, you can be sure that those figures weren't pulled out of nowhere.
Acquiring data for risk assessment Risk Management Solutions (RMS) Inc, founded at Stanford University in 1988, has built its reputation on worldwide catastrophe modeling technology. RMS founder Haresh Shah is chairman emeritus of Stanford's Civil Engineering Dept. Its technology is used by insurers, industrial corporations, governments, and financial institutions, comprising 50 per cent of the worldwide market, to manage their exposure to highly volatile risks.
At RMS India, the Risk Management Group is a team of civil engineers, meteorologists, statisticians, economists, and software engineers who develop quantitative models for evaluating risks stemming from natural disasters. One of the projects that RMSI engineers have been working on is a model for estimation of losses due to earthquakes.
RMSI has conducted site surveys at Chamoli and Latur (where major earthquakes were experienced) to collect first hand data on damages. This data is being used to initiate the development of an India-specific earthquake model.
When the earthquake hit Latur in Maharashtra in 1989, RMSI undertook a computer-aided case study, which included a collection of village-level hazard and loss data, mapping of affected villages with respect to the epicenter, and study of the worst-hit region. RMSI also gathered rich experience in earthquake modelling due to its involvement in the Taiwan earthquake model development project. The objective was to develop a system for estimating earthquakes losses for the insurance industry in Taiwan.
What timely information can do By applying complex mathematical algorithms to simple measurable data, expert systems can generate possible scenarios for different catastrophies in various locations. The models are built upon detailed databases describing various hazards with respect to their physical variables, as well as databases capturing property inventory, building stock, and insurance exposures.
The information thus generated is used by governments to estimate losses and enable quick and fairly accurate planning which aids in mitigating losses and expediting rehabilitation processes. The insurance industry too uses catastrophy management models to manage their risks through estimation of probable losses due to earthquake or floods.
All said and done, modern catastrophe modelling techniques can only alleviate the economic after-shocks of earthquakes.
Regretably, technology does not let us choose prevention over cure. At least not yet.