Bangalore: Scientists at IBM Research Labs have developed specialised math algorithms to help model and manage natural disasters including wildfires, floods and diseases.<!--more-->

While the algorithm would enable scientist to model flood scenarios in terms of their severity, location and spread characteristics, it would also help in optimising resource acquisition and deployment decisions.

It also helps obtain strategic resource organisation recommendations and tactical optimal deployment decisions.

According to IBM, the ‘stochastic optimisation model' was developed by its math scientists in New York and India who worked together with business experts from IBM's Global Business Services and 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 mathematicians magic potion is bottled up in complex algorithms—essentially math equations—that helps speed up and simplify complex tasks into everyday life such as determining the fastest route to deliver packages and detecting fraud in health insurance claims.

It also helps in automating complex risk decisions for international financial institutions, scheduling supply chain and production at a manufacturing plant to maximise efficiency or detecting patterns in medical data for new insights and breakthroughs.

Talking about the project, IBM India Research Laboratory Director Daniel Dias said that the challenge lies in matching high-end mathematical programming technologies with high-impact business and societal problems, while using open platforms and standards.

"Our researchers have worked on innovative optimisation solutions designed to create a roadmap for a responsive disaster risk reduction," he added.

The deployment of resources during a natural disaster, whether it be water, food, machines, or people, 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.

The government agencies use different systems to estimate their programme needs, including preparedness resource planning, yet no one system has been able to adapt to the increasing complexity of natural disaster management, IBM stated.

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 optimisation models and algorithms were initially prototyped on a large US government programme, 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 a decision support system to allow decision makers to consider multiple solutions to each disaster scenario, so that a range of solution alternatives can be generated by the system.

The same models can be explored to manage floods or famines in India, or natural disasters anywhere in the world, the company said.

A fully developed, customised 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," IBM India Research Laboratory Senior Researcher and Optimisation Expert Gyana Parija said.

In the case of flooding, for example, 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, IBM stated.

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, the company added.

Stochastic programming offers greater modelling 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.
—iGovernment Bureau