IBM’s scientists have created specialized math algorithms to help model and manage natural disasters: wildfires, floods, diseases and more.
IBM’s ‘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 and directly with clients to arm government bodies, relief agencies and companies with tools for strategic planning for more effective allocation of resources for natural disaster management and mitigation.
The deployment of resources during a natural disaster, whether it be water, food, machines, people or something else, requires complex planning and scheduling and the need to adapt to constantly changing scenarios, often involving large number of resources, unique requirements based on location and the varying staffing levels associated with each resource. Government agencies use different systems to estimate their program needs, including preparedness resource planning, yet no one system has been able to adapt to the increasing complexity of natural disaster management.
These challenges resulted in IBM developing 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 event scenarios. That system generated a single solution for each disaster scenario. The current enhancements to the budgeting system include the development of simulation models to assess and evaluate the impact of alternative strategies based upon criteria selected by the client. Optimization allows the client to trade off among multiple priorities to understand the impacts to performance measures.
The same models can be explored to manage floods or famines in India, or natural disasters anywhere in the world. 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.”
In the case of flooding, the stochastic programming model would use various flood scenarios, resource supply capabilities at different dispatch locations, and fixed and variable costs associated with deployment of various flood-management resources to manage various risk measures. By assigning probabilities to the factors driving outcomes, the model outlines how limited resources can meet tomorrow’s unknown demands or liabilities. In this way, the risks and rewards of various tradeoffs can be explored.
Stochastic programming offers greater modeling power and flexibility, but 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, and has very good scalability that promises to gracefully manage even larger models in the future.
As stochastic models become more sophisticated, researchers like IBM’s Dr. Gyana Parija have been able to infuse the models with “human” factors, such as politics, custom and culture. As researchers factor in human behavior in the models, the results grow less uncertain and more accurate and acceptable.