“Data and machine learning approaches to triage disaster responses”
The problem being addressed
When a natural disaster strikes, Government and response agencies need rapid information on the damage (affected population, casualties, road blocks, flood extend, damaged houses). The information that is presented to decision-makers in the wake of a disaster needs to be accurate, appropriate, timely and valid.
It is essential to identify priority areas, by assessing damage and finding vulnerable people that are affected the most. Currently damage assessments and identification of the most vulnerable is a time consuming process, which can take weeks to complete (site visits, interviews etc), due to logistics, safety constraints, or workload, furthermore their accuracy can sometimes be questionable.
How we want to Address it?
Our aim is to develop a methodology to identify high priority areas for humanitarian response, based on (open) secondary data of affected areas, combined with disaster impact data (such as windspeeds and rain) and by learning from past disasters. It is important that we invest in data preparedness, so that these pre-crisis secondary datasets are available and up-to-date.
Our objective is to develop machine learning methodologies that can be applied to different countries, using local data, and with minor modifications reach a fast and sufficiently accurate damage prediction. Applied research on this objective is ongoing for Typhoons (Philippines), Earthquakes (Nepal) and Floods (Malawi).
In the risk management domain probabilistic models are being developed for determining the likelihood of losses from a disaster (usually economic loss). It creates impact scenario’s that can be used by decision makers to mitigate risk. These models however are not developed to predict impact on people during a recent disaster. Our approach is not to develop sophisticated hydrologic, seismic, or windspeed models, but to use machine learning methods to find the best predictors in existing base line data to predict typhoon impact. Specifically we want to focus on;
- Improving the performance, and reducing the error, of the prediction model and;
- Reducing the time needed to release a prediction on damage after a new typhoon