Predicting natural disasters

Thursday, April 2, 2015

A new publication emphasizing the role of predictive analytical capabilities in natural disaster management, co-authored with M. Castelli and L. Vanneschi (2015): Predicting Burned Areas of Forest Fires: An Artificial Intelligence Approach appeared in one of the most prestigious international journals in fire ecology field. Abstract:

The ability of accurately predicting forest fire areas may significantly aid optimizing fire management efforts. Given the complexity of the task, powerful computational tools are needed for predicting the amount of area that will be burned during a forest fire. The purpose of this study was to develop an intelligent system based on genetic programming for the prediction of burned areas, using only data related to the forest under analysis and meteorological data. We used geometric semantic genetic programming based on recently defined geometric semantic genetic operators for genetic programming. Experimental results showed the appropriateness of the proposed system for the prediction of the burned areas. In particular, the obtained results were significantly better than those produced by standard genetic programming and other state of the art machine learning methods.

Aleš Popovič

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