IBM math algorithms for natural disasters management

By siliconindia   |   Wednesday, 02 April 2008, 05:01 Hrs
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New Delhi: IBM has announced that its scientists have created specialized math algorithms to help manage natural disasters like wildfires, floods and diseases. 'Stochastic optimization model' was developed by IBM math scientists from IBM Research Labs in New York and India working with business experts from IBM's Global Business Services for strategic planning for more effective allocation of resources for natural disaster management and mitigation. IBM's math team works on problems in business, government and society. "The challenge lies in matching high-end mathematical programming technologies with high-impact business & societal problems, while using open platforms and standards. Our researchers have worked on optimization solutions designed to create a roadmap for a responsive disaster risk reduction," said Dr. Daniel Dias, Director, IBM India Research Laboratory. To tackle this challenges IBM developed a large-scale strategic budgeting framework for managing natural disaster events, with a focus on better preparedness for future uncertain disaster scenarios. The underlying optimization models and algorithms were initially prototyped on a large U.S. Government program, where the key problem was how to efficiently deploy a large number of critical resources to a range of disaster situations. A fully developed, customized and implemented model can significantly help the country's approach for disaster risk reduction and disaster management. "We are creating a set of intellectual properties and software assets that can be employed to gauge and improve levels of preparedness to tackle unforeseen natural disasters," says Dr. Gyana Parija, senior researcher and optimization expert at IBM India Research Laboratory, New Delhi. "Most real-world problems involve uncertainty, and this has been the inspiration for us to tackle challenges in natural disaster management." A press release said that though stochastic programming offers greater modeling power and flexibility, it comes at a cost-premium processing time. However, recently, stochastic programming has benefited from the development of more efficient algorithms and faster computer processors. This means that rather than predicting a limited future using forecasting, decisions supporting a wide range of probable scenarios can be taken. The model allows all unforeseen challenges to be solved, mostly within an hour.

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